hierarchical generalised linear models a synthesis of generalised linear models random effect models and structured dispersions summary hierarchical generalised linear models are developed as a synthesis of generalised linear models mixed linear models and structured dispersions we generalise the restricted maximum likelihood method for the estimation of dispersion to the wider class and show how the joint fitting of models for mean and dispersion can be expressed by two interconnected generalised linear models the method allows models with i any combination of a generalised linear model distribution for the response with any conjugate distribution for the random effects ii structured dispersion components iii different link and variance functions for the fixed and random effects and iv the use of quasilikelihoods in place of likelihoods for either or both of the mean and dispersion models inferences can be made by applying standard procedures in particular those for model checking to components of either generalised linear model we also show by numerical studies that the new method gives an efficient estimation procedure for substantial class of models of practical importance likelihood type inference is extended to this wide class of models in a unified way

optimal design of constraint following control for fuzzy mechanical systems the control problem for a mechanical system to follow a constraint is formulated the uncertainty is considered in both the system dynamics and constraint it is bounded and the knowledge of the bound only lies within a fuzzy set we propose an adaptive robust control for the mechanical system for approximate constraint following both the mechanical system model and control scheme are deterministic and not if then heuristic rules based an optimal design problem using the fuzzy description of the uncertainty is proposed we prove that the global solution to the resulting optimization problem always exists and is unique under the control the performance of the mechanical system is both deterministically guaranteed and fuzzily optimized

estimating a state space model from point process observations a widely used signal processing paradigm is the state space model the state space model is defined by two equations an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit latent stimulus we develop an algorithm to estimate a state space model observed through point process measurements we represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus given the latent process neural spiking activity is characterized as a general point process defined by its conditional intensity function we develop an approximate expectation maximization em algorithm to estimate the unobservable state space process its parameters and the parameters of the point process the em algorithm combines a point process recursive nonlinear filter algorithm the fixed interval smoothing algorithm and the state space covariance algorithm to compute the complete data log likelihood efficiently we use a kolmogorov smirnov test based on the time rescaling theorem to evaluate agreement between the model and point process data we illustrate the model with two simulated data examples an ensemble of poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local bernoulli process

a non gaussian kalman filter with application to the estimation of vehicular speed single inductive loop detectors ilds which provide online measurements of traffic volume and occupancy are widely used devices in road systems because of the nature of traffic flow fast estimation and forecasting of vehicular speed using the data collected by an ild are crucial to online road traffic management here we formulate statistical inference for vehicular speed as a dynamic generalized linear model with a reciprocal inverse gaussian observational distribution the formulation motivates us to extend the gaussian kalman filter to this non gaussian scenario this results in a set of simple recursive formulas in which the current estimate of the parameter of interest is updated as a weighted harmonic average of the previous estimate and the current observation by applying the developed non gaussian kalman filter to analyze traffic data collected by an ild we provide a competitive alternative for estimating vehicular speed at a minimum computational cost

a bootstrap method for structure detection of narmax models many systems may be described by narmax models using only a few terms however depending on the order of the system the number of candidate terms can become very large selection of a subset of these candidate terms is necessary for an efficient system description this is an unresolved issue in system identification for over parameterized models therefore in this paper we develop a bootstrap structure detection bsd algorithm as a means of determining the structure of highly over parameterized models the performance of this bsd technique was evaluated by using it to estimate the structure of a 1 simple narmax model 2 moderately over parameterized narmax model and 3 highly over parameterized narmax model the results demonstrate that the bsd algorithm is a robust method for detecting the structure of narmax models this method provides accurate estimates of parameter statistics without relying on assumptions made by traditional procedures and yields a parsimonious description of the system

a new perspective on the tracking control of nonlinear structural and mechanical systems based on recent results from analytical dynamics this paper develops a class of tracking controllers for controlling general nonlinear structural and mechanical systems unlike most control methods that perform some kind of linearization and or nonlinear cancellation the methodology developed herein views the nonlinear control problem from a different perspective this leads to a simple and new control methodology that is capable of exactly maintaining the nonlinear system along a certain trajectory which in general may be described by a set of differential equations in the observations measurements the approach requires very little computation compared with standard approaches it is therefore useful for online real time control of nonlinear systems the methodology is illustrated with two examples

stable bundles and integrable systems on considere la geometrie symplectique des fibres cotangents aux espaces de modules de fibres vectoriels stables sur une surface de riemann on montre que ce sont des systemes dynamiques hamiltoniens algebriquement completement integrables

functions analytic on the half plane as quantum mechanical states a transform between the state space of one dimensional quantum mechanical systems and a direct sum of two spaces of square integrable functions analytic on the open upper half plane is constructed it gives a representation of usual quantum mechanics on which the free evolution is trivial and the representation of canonical transformation very simple generalizations to higher dimensions are also discussed

estimation of stochastic volatility models via monte carlo maximum likelihood this paper discusses the monte carlo maximum likelihood method of estimating stochastic volatility sv models the basic sv model can be expressed as a linear state space model with log chi square disturbances the likelihood function can be approximated arbitrarily accurately by decomposing it into a gaussian part constructed by the kalman filter and a remainder function whose expectation is evaluated by simulation no modifications of this estimation procedure are required when the basic sv model is extended in a number of directions likely to arise in applied empirical research this compares favorably with alternative approaches the finite sample performance of the new estimator is shown to be comparable to the monte carlo markov chain mcmc method

optimized wavelet denoising for self similar alpha stable processes we investigate the performance of wavelet shrinkage methods for the denoising of symmetric alpha stable text s alpha text s self similar stochastic processes corrupted by additive white gaussian noise awgn where alpha is tied to the sparsity of the process the wavelet transform is assumed to be orthonormal and the shrinkage function minimizes the mean square approximation error mmse estimator we derive the corresponding formula for the expected value of the averaged estimation error we show that the predicted mmse is a monotone function of a simple criterion that depends on the wavelet and the statistical parameters of the process using the calculus of variations we then optimize this criterion to find the best performing wavelet within the extended family of meyer wavelets which are bandlimited these are compared with the daubechies wavelets which are compactly supported in time we find that the wavelets that are shorter in time in particular the haar basis are better suited to denoise the sparser processes say alpha while the bandlimited ones including the held and shannon wavelets offer the best performance for alpha 1 6 the limit corresponding to the gaussian case fbm with alpha 2

a wavelet or lifting scheme based imputation method summary the paper proposes a new approach to imputation using the expected sparse representation of a surface in a wavelet or lifting scheme basis our method incorporates a bayesian mixture prior for these wavelet coefficients into a gibbs sampler to generate a complete posterior distribution for the variable of interest intuitively the estimator operates by borrowing strength from those observed neighbouring values to impute at the unobserved sites we demonstrate the strong performance of our estimator in both one and two dimensional imputation problems where we also compare its application with the standard imputation techniques of kriging and thin plate splines

state dependent parameter models of non linear sampled data systems a velocity based linearization approach discrete time models with state dependent parameters are derived for non linear sampled data systems velocity based linearization of the continuous time system followed by integration over the sampling interval is used to represent the sampled data system by a discrete time state space model with state dependent parameters by reconstructing the state a quasi arx model is obtained the parameters of which are functions of past inputs and outputs as the velocity based linearization procedure does not involve suppression of higher order terms the state dependent state space model and the quasi arx description give exact representations of the non linear system

refined instrumental variable estimation for many years various methods for the identification and estimation of parameters in linear discrete time transfer functions have been available and implemented in widely available toolboxes for matlab this paper considers a unified refined instrumental variable riv approach to the estimation of discrete and continuous time transfer functions characterized by a unified operator that can be interpreted in terms of backward shift derivative or delta operators the estimation is based on the formulation of a pseudo linear regression relationship involving optimal prefilters that is derived from an appropriately unified box jenkins transfer function model the paper shows that contrary to apparently widely held beliefs the iterative riv algorithm provides a reliable solution to the maximum likelihood optimization equations for this class of box jenkins transfer function models and so its en bloc or recursive parameter estimates are optimal in maximum likelihood prediction error minimization and instrumental variable terms

particle efficient importance sampling abstract the efficient importance sampling eis method is a general principle for the numerical evaluation of high dimensional integrals that uses the sequential structure of target integrands to build variance minimising importance samplers despite a number of successful applications in high dimensions it is well known that importance sampling strategies are subject to an exponential growth in variance as the dimension of the integration increases we solve this problem by recognising that the eis framework has an offline sequential monte carlo interpretation the particle eis method is based on non standard resampling weights that take into account the construction of the importance sampler as a sequential approximation to the state smoothing density we apply the method for a range of univariate and bivariate stochastic volatility specifications we also develop a new application of the eis approach to state space models with student s t state innovations our results show that the particle eis method strongly outperforms both the standard eis method and particle filters for likelihood evaluation in high dimensions we illustrate the efficiency of the method for bayesian inference using the particle marginal metropolis hastings and importance sampling squared algorithms

state space control system design based on non minimal state variable feedback further generalization and unification results this paper shows how proportional integral plus linear quadratic pip lq control based on non minimal state space nmss control system design can be constrained to yield exactly the same control algorithm as both generalized predictive control gpc and standard minimal state linear quadratic gaussian lqg design methods however while nmss includes these other approaches as special cases it is less constrained and so more flexible in general terms for example while pip lq has the simplicity of gpc it is formulated like lqg in the powerful context of state variable feedback svf control which allows for ready access to modern robust control methods furthermore the paper suggests that the nmss approach provides the greater design freedom with a wider range of possible lq solutions than its minimal state space equivalent

monte carlo methods for estimating smoothing and filtering one and two factor stochastic volatility models one and two factor stochastic volatility models are assessed over three sets of stock returns data sp the problem is to get the shape of the conditional returns distribution right none of the models come close to matching the tails of this distribution including a second factor provides only a relatively small improvement over the single factor models fitting this aspect of the data is important for option pricing and risk management

variable selection via rival removing irrelevant variables amidst lasso iterations and its application to nuclear material detection in many situations the number of data points is fixed and the asymptotic convergence results of popular model selection tools may not be useful a new algorithm for model selection rival removing irrelevant variables amidst lasso iterations is presented and shown to be particularly effective for a large but fixed number of data points the algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non negative thus positive lasso and its variants are analyzed then rival is proposed and is shown to have some desirable properties namely the number of data points needed to have convergence is smaller than existing methods

design of regular nonseparable bidimensional wavelets using grobner basis techniques the design of two dimensional 2 d filter banks yielding orthogonality and linear phase filters and generating regular wavelet bases is a difficult task involving the algebraic properties of multivariate polynomials using cascade forms implies dealing with nonlinear optimization we turn the issue of optimizing the orthogonal linear phase cascade from kovacevic and vetterli 1992 into a polynomial problem and solve it using grobner basis techniques and computer algebra this leads to a complete description of maximally flat wavelets among the orthogonal linear phase family proposed by kovacevic and vetterli we obtain up to five degrees of flatness for a 16 spl times 16 filter bank whose sobolev exponent is 2 11 making this wavelet the most regular orthogonal linear phase nonseparable wavelet to the authors knowledge

solution of matrix riemann hilbert problems with quasi permutation monodromy matrices in this paper we solve an arbitrary matrix riemann hilbert inverse monodromy problem with irreducible quasi permutation monodromy representation outside of a divisor in the space of monodromy data this divisor is characterized in terms of the theta divisor on the jacobi manifold of an auxiliary compact riemann surface realized as an appropriate branched covering of open image in new windowp1 the solution is given in terms of a generalization of szego kernel on the riemann surface in particular our construction provides a new class of solutions of the schlesinger system the isomonodromy tau function of these solutions is computed up to a nowhere vanishing factor independent of the elements of monodromy matrices results of this work generalize the results of papers 13 and 14 where the 2 2 case was solved

painless nonorthogonal expansions in a hilbert space h discrete families of vectors hj with the property that f j hj f hj for every f in h are considered this expansion formula is obviously true if the family is an orthonormal basis of h but also can hold in situations where the hj are not mutually orthogonal and are overcomplete the two classes of examples studied here are i appropriate sets of weyl heisenberg coherent states based on certain non gaussian fiducial vectors and ii analogous families of affine coherent states it is believed that such quasiorthogonal expansions will be a useful tool in many areas of theoretical physics and applied mathematics

wavelet sampling and localization schemes for the radon transform in two dimensions two theorems are presented for wavelet decompositions of the two dimensional radon transform the rst theorem establishes an upper error bound in l 2 norm between the radon transform and its wavelet approximation whose coecients at dierent scales are estimated from radon data acquired at corresponding sampling rates the second theorem gives an estimate of the accuracy of a local image reconstructed from localized radon data at multiple levels these results show how to design a multilevel sampling and localization strategy for parallel beam scanning by using wavelet regularity and vanishing moment characteristics clarify the interaction between the wavelet structure and the essential bandwidth of an object image and provide guidelines for wavelet local tomography

stochastic model specification search for gaussian and partial non gaussian state space models model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time varying to this aim a new model space mcmc method is developed in this paper it is based on extending the bayesian variable selection approach which is usually applied to variable selection in regression models to state space models for non gaussian state space models stochastic model search mcmc makes use of auxiliary mixture sampling we focus on structural time series models including seasonal components trend or intervention the method is applied to various well known time series

identification of state dependent parameter models with support vector regression a support vector regression approach is presented for the identification of state dependent parameter arx models whose parameters are described as functions of past inputs and outputs the problem of identifying the state dependent parameters reduces to a standard support vector regression problem with a kernel function which is defined in terms of the kernels used to represent the individual parameters numerical examples show that the support vector method gives accurate parameter estimates for systems which have a state dependent parameter representation

time series analysis of non gaussian observations based on state space models from both classical and bayesian perspectives the analysis of non gaussian time series by using state space models is considered from both classical and bayesian perspectives the treatment in both cases is based on simulation using importance sampling and antithetic variables markov chain monte carlo methods are not employed non gaussian disturbances for the state equation as well as for the observation equation are considered methods for estimating conditional and posterior means of functions of the state vector given the observations and the mean square errors of their estimates are developed these methods are extended to cover the estimation of conditional and posterior densities and distribution functions the choice of importance sampling densities and antithetic variables is discussed the techniques work well in practice and are computationally efficient their use is illustrated by applying them to a univariate discrete time series a series with outliers and a volatility series

transforms associated to square integrable group representations i general results let g be a locally compact group which need not be unimodular let x u x x g be an irreducible unitary representation of g in a hilbert space h u assume that u is square integrable i e that there exists in h u at least one nonzero vector g such that u x g g 2 dx we give here a reasonably self contained analysis of the correspondence associating to every vector f h u the function u x g f on g discussing its isometry characterization of the range inversion and simplest interpolation properties this correspondence underlies many properties of generalized coherent states

symplectic geometry of the moduli space of projective structures in homological coordinates we study the symplectic geometry of the space of linear differential equations with holomorphic coefficients of the form varphi u varphi 0 on riemann surfaces of genus g this space coincides with the moduli space of projective connections which is an affine bundle modelled on the cotangent bundle t mathcal m g we show that for several choices of the origin or base holomorphic projective connection such as bergman wirtinger or schottky the canonical poisson structure on t mathcal m g induces the goldman bracket on the monodromy character variety these different choices give rise to equivalent symplectic structures on the space of projective connections but different symplectic polarizations we find the corresponding generating functions combined with a prior theorem of kawai our results show the symplectic equivalence between the embeddings of t mathcal m g induced by the bers and bergman projective connections into the space of projective structures the main technical tools are variational formulas with respect to homological darboux coordinates on the space of holomorphic quadratic differentials in particular we get a new system of differential equations for the prym matrix of the canonical two sheeted covering of a riemann surface defined by a quadratic differential

auxiliary mixture sampling for parameter driven models of time series of counts with applications to state space modelling we consider parameter driven models of time series of counts where the observations are assumed to arise from a poisson distribution with a mean changing over time according to a latent process estimation of these models is carried out within a bayesian framework using data augmentation and markov chain monte carlo methods we suggest a new auxiliary mixture sampler which possesses a gibbsian transition kernel where we draw from full conditional distributions belonging to standard distribution families only emphasis lies on application to state space modelling of time series of counts but we show that auxiliary mixture sampling may be applied to a wider range of parameter driven models including random effects models and panel data models based on the poisson distribution

prediction error identification of linear systems a nonparametric gaussian regression approach a novel bayesian paradigm for the identification of output error models has recently been proposed in which in place of postulating finite dimensional models of the system transfer function the system impulse response is searched for within an infinite dimensional space in this paper such a nonparametric approach is applied to the design of optimal predictors and discrete time models based on prediction error minimization by interpreting the predictor impulse responses as realizations of gaussian processes the proposed scheme describes the predictor impulse responses as the convolution of an infinite dimensional response with a low dimensional parametric response that captures possible high frequency dynamics overparameterization is avoided because the model involves only a few hyperparameters that are tuned via marginal likelihood maximization numerical experiments with data generated by armax and infinite dimensional models show the definite advantages of the new approach over standard parametric prediction error techniques and subspace methods both in terms of predictive capability on new data and accuracy in reconstruction of system impulse responses

identification of non linear stochastic systems by state dependent parameter estimation the paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques with the associated hyper parameters optimized by maximum likelihood based on prediction error decomposition it then shows how by exploiting special data re ordering and back fitting procedures similar recursive parameter estimation techniques can be utilized to estimate much more rapid state dependent parameter sdp variations in this manner it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems as illustrated by several examples that include simulated and real data from chaotic processes finally the paper points out that such sdp models can form the basis for new methods of signal processing automatic control and state estimation for nonlinear stochastic systems

a new class of biorthogonal wavelet systems for image transform coding we construct general biorthogonal coifman wavelet systems a new class of compactly supported biorthogonal wavelet systems with vanishing moments equally distributed for a scaling function and wavelet pair a time domain design method is employed and closed form expressions for the impulse responses and the frequency responses of the corresponding dual filters are derived the resulting filter coefficients are all dyadic fractions which is an attractive feature in the realization of multiplication free discrete wavelet transform even ordered systems in this family are symmetric which correspond to linear phase dual filters in particular three filterbanks fbs in this family are systematically verified to have competitive compression potential to the 9 7 tap biorthogonal wavelet fb by cohen et al 1992 which is currently the most widely used one in the field of wavelet transform coding in addition the proposed fb s have much smaller computational complexity in terms of floating point operations required in transformation and therefore indicate a better tradeoff between compression performance and computational complexity

application of a least absolute shrinkage and selection operator to aeroelastic flight test data identification of non linear systems involves estimating unknown parameters and model regressor selection selection of a subset of candidate terms that best describes the observed output model selection is an important step in black box modelling of any observed process this procedure is concerned with selecting a subset of parameters to give a parsimonious description of the system which may afford greater insight into the functionality of the system or a simpler controller design in this study a least absolute shrinkage and selection operator lasso technique is investigated for computing efficient model descriptions of non linear aeroelastic systems the lasso minimises the residual sum of squares by the addition of an l1 penalty term on the parameter vector of the traditional l2 minimisation problem its use for model selection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and the

the structure of dynamic correlations in multivariate stochastic volatility models this paper proposes two types of stochastic correlation structures for multivariate stochastic volatility msv models namely the constant correlation cc msv and dynamic correlation dc msv models from which the stochastic covariance structures can easily be obtained both structures can be used for purposes of determining optimal portfolio and risk management strategies through the use of correlation matrices and for calculating value at risk var forecasts and optimal capital charges under the basel accord through the use of covariance matrices a technique is developed to estimate the dc msv model using the markov chain monte carlo mcmc procedure and simulated data show that the estimation method works well various multivariate conditional volatility and msv models are compared via simulation including an evaluation of alternative var estimators the dc msv model is also estimated using three sets of empirical data namely nikkei 225 index hang seng index and straits times index returns and significant dynamic correlations are found the dynamic conditional correlation dcc model is also estimated and is found to be far less sensitive to the covariation in the shocks to the indexes the correlation process for the dcc model also appears to have a unit root and hence constant conditional correlations in the long run in contrast the estimates arising from the dc msv model indicate that the dynamic correlation process is stationary

fixed interval smoothing problem from uncertain observations with correlated signal and noise this paper presents recursive filtering and fixed interval smoothing algorithms from observations corrupted by additive and multiplicative noises additive noise is a white process correlated with the signal and multiplicative noise is modelled by a sequence of independent bernoulli random variables it is assumed that both autocovariance function of signal and crosscovariance function about signal and observation noise are expressed in a semi degenerate kernel form the algorithms are obtained by an innovation approach without using the state space model but only covariance information of signal and observation noise and probability that signal exists in the observed values

on the least asymmetric wavelets the asymmetry of daubechies 1988 1992 scaling functions and wavelets can be diminished by minimizing a special second moment in time for the wavelet generating discrete time filter the moment is involved in an uncertainty relation for discrete time signals other measures of asymmetry are addressed as well and corresponding results are compared

connected components of the moduli spaces of abelian differentials with prescribed singularities consider the moduli space of pairs c where c is a smooth compact complex curve of a given genus and is a holomorphic 1 form on c with a given list of multiplicities of zeroes we describe connected components of this space this classification is important in the study of dynamics of interval exchange transformations and billiards in rational polygons and in the study of geometry of translation surfaces

observation driven models for poisson counts summary this paper is concerned with a general class of observation driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a poisson distribution these models provide a flexible framework for modelling a wide range of dependence structures conditions for stationarity and ergodicity of these processes are established from which the large sample properties of the maximum likelihood estimators can be derived simulations are provided to give additional insight into the finite sample behaviour of the estimators finally an application to a regression model for daily counts of asthma presentations at a sydney hospital is described

comments on identification of non linear parametrically varying models using separable least squares by f previdi and m lovera black box or open box this note compares and contrasts the non linear parameter varying nlpv and state dependent parameter sdp model classes it shows that while they have similarities the two stage sdp modelling procedure involving non parametric identification followed by parametric estimation is quite different from the single stage nlpv procedure in particular the sdp procedure allows for the identification of the model structure and the nature of the non linearities prior to the estimation of the parameters that characterize this identified model structure in contrast to nlpv modelling therefore sdp estimation opens up the black box and reveals the inner nature of the non linear system

multiscale poisson data smoothing summary the paper introduces a framework for non linear multiscale decompositions of poisson data that have piecewise smooth intensity curves the key concept is conditioning on the sum of the observations that are involved in the computation of a given multiscale coefficient within this framework most classical wavelet thresholding schemes for data with additive homoscedastic noise can be used any family of wavelet transforms orthogonal biorthogonal or second generation can be incorporated in this framework our second contribution is to propose a bayesian shrinkage approach with an original prior for coefficients of this decomposition as such the method combines the advantages of the haar fisz transform with wavelet smoothing and bayesian multiscale likelihood models with additional benefits such as extendability towards arbitrary wavelet families simulations show an important reduction in average squared error of the output compared with the present techniques of anscombe or fisz variance stabilization or multiscale likelihood modelling

markov chain monte carlo methods for stochastic volatility models this paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy tailed student t distributions with unknown degrees of freedom and exogenous variables in the observation and volatility equations and a jump component in the observation equation by building on the work of kim shephard and chib rev econom stud 65 1998 361 we develop efficient markov chain monte carlo algorithms for estimating these models the paper also discusses how the likelihood function of these models can be computed by appropriate particle filter methods computation of the marginal likelihood by the method of chib j amer statist assoc 90 1995 1313 is also considered the methodology is extensively tested and validated on simulated data and then applied in detail to daily returns data on the s p 500 index where several stochastic volatility models are formally compared under different priors on the parameters

nonlocal image restoration with bilateral variance estimation a low rank approach simultaneous sparse coding ssc or nonlocal image representation has shown great potential in various low level vision tasks leading to several state of the art image restoration techniques including bm3d and lssc however it still lacks a physically plausible explanation about why ssc is a better model than conventional sparse coding for the class of natural images meanwhile the problem of sparsity optimization especially when tangled with dictionary learning is computationally difficult to solve in this paper we take a low rank approach toward ssc and provide a conceptually simple interpretation from a bilateral variance estimation perspective namely that singular value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular value thresholding saist for noise data saist generalizes the celebrated bayesshrink from local to nonlocal models for incomplete data saist extends previous deterministic annealing based solution to sparsity optimization through incorporating the idea of dictionary learning in addition to conceptual simplicity and computational efficiency saist has achieved highly competent often better objective performance compared to several state of the art methods in image denoising and completion experiments our subjective quality results compare favorably with those obtained by existing techniques especially at high noise levels and with a large amount of missing data

non linear control by input output state variable feedback pole assignment this article considers pole assignment control of non linear dynamic systems described by state dependent parameter sdp models the approach follows from earlier research into linear proportional integral plus methods but in sdp system control the control coefficients are updated at each sampling instant on the basis of the latest sdp relationships alternatively algebraic solutions can be derived off line to yield a practically useful control algorithm that is relatively straightforward to implement on a digital computer requiring only the storage of delayed system variables coupled with straightforward arithmetic expressions in the control software although the analysis is limited to the case when the open loop system has no zeros time delays are handled automatically this article shows that the closed loop system reduces to a linear transfer function with the specified design poles hence assuming pole assignability at each sample global stability of the non linear system is guaranteed at

approximating hidden gaussian markov random fields summary gaussian markov random field gmrf models are frequently used in a wide variety of applications in most cases parts of the gmrf are observed through mutually independent data hence the full conditional of the gmrf a hidden gmrf hgmrf is of interest we are concerned with the case where the likelihood is non gaussian leading to non gaussian hgmrf models several researchers have constructed block sampling markov chain monte carlo schemes based on approximations of the hgmrf by a gmrf using a second order expansion of the log density at or near the mode this is possible as the gmrf approximation can be sampled exactly with a known normalizing constant the markov property of the gmrf approximation yields computational efficiency the main contribution in the paper is to go beyond the gmrf approximation and to construct a class of non gaussian approximations which adapt automatically to the particular hgmrf that is under study the accuracy can be tuned by intuitive parameters to nearly any precision these non gaussian approximations share the same computational complexity as those which are based on gmrfs and can be sampled exactly with computable normalizing constants we apply our approximations in spatial disease mapping and model based geostatistical models with different likelihoods obtain procedures for block updating and construct metropolized independence samplers

improved transient signal detection using a wavepacket based detector with an extended translation invariant wavelet transform this paper presents the theory of m band extended translation invariant eti wavelet transforms the eti generalizes the translation invariant wavelet transform of weiss 1933 it is shown that iteration of the eti in a tree structure provides a signal decomposition into an orthonormal wavepacket basis other properties such as translation invariance and invertibility of the transform are proven the theory is then applied to transient signal detection through development of a family of translation invariant wavepacket based detectors this family of detectors provides improved the performance over previously defined wavepacket based detectors a performance analysis is conducted roc curves generated by monte carlo simulation are presented indicating the detector performance the detector performance is demonstrated to be independent of the signal translation

wavelet based linear system modeling and adaptive filtering it is shown how linear time varying systems can be modeled in several different ways by discrete time wavelets or more generally by some set of functions interpretation of physical meanings possible efficiency and other characteristics of the modeling are considered system identification minimizing the mean square output error is studied optimal coefficients and the corresponding minimum mean square error are found and they are in general time varying least mean square adaptive filtering algorithms are derived for on line filtering and system identification theoretically and by simulations the advantages of using wavelet based filtering are shown separation of adaptation effects from unknown time varying system behavior and fast convergence adaptive coefficients estimated by a recursive least square algorithm can tend toward constants even in the case of time varying systems time invariant system identification and adaptive filtering is given as a special case of the general time varying setting

particle approximations of the score and observed information matrix in state space models with application to parameter estimation particle methods are popular computational tools for bayesian inference in nonlinear non gaussian state space models for this class of models we present two particle algorithms to compute the score vector and observed information matrix recursively the first algorithm is implemented with computational complexity o n and the second with complexity o n 2 where n is the number of particles although cheaper the performance of the o n method degrades quickly as it relies on the approximation of a sequence of probability distributions whose dimension increases linearly with time in particular even under strong mixing assumptions the variance of the estimates computed with the o n method increases at least quadratically in time the more expensive o n 2 method relies on a nonstandard particle implementation and does not suffer from this rapid degradation it is shown how both methods can be used to perform batch and recursive parameter estimation

hammerstein system identification through best linear approximation inversion and regularisation abstracthammerstein systems are composed by the cascading of a static nonlinearity and a linear system in this paper a methodology for identifying such systems using a combination of least squares support vector machines ls svm and best linear approximation bla techniques is proposed to do this a novel method for estimating the intermediate variable is presented allowing a clear separation of the identification steps first an approximation to the linear block is obtained through the bla of the system then an approximation to the intermediate variable is obtained using the inversion of the estimated linear block and the known output afterwards a nonlinear model is calculated through ls svm using the estimated intermediate variable and the known input to do this the regularisation capabilities of ls svm play a crucial role finally a parametric re estimation of the linear block is made the method was tested in three examples two of them with hard nonlinearities and was compared with four

the self duality equations on a riemann surface in this paper we shall study a special class of solutions of the self dual yang mills equations the original self duality equations which arose in mathematical physics were defined on euclidean 4 space the physically relevant solutions were the ones with finite action the so called instantons the same equations may be dimensionally reduced to euclidean 3 space by imposing invariance under translation in one direction these equations also have physical relevance the solutions which have finite action in three dimensions are the magnetic monopoles if we take the reduction process one step further and consider solutions which are invariant under two translations we obtain a set of equations in the plane here however there is no clear physical meaning and indeed attempts to find finite action solutions have failed nevertheless these are the equations we shall consider despite the lack of interesting solutions in r2 the equations have the important property conformal invariance which allows them to be defined on manifolds modelled on r2 by conformal maps namely riemann surfaces we shall consider here solutions of the self duality equations defined on a compact riemann surface there are in fact solutions as we shall show and the moduli space of all solutions turns out to be a manifold with an extremely rich geometric structure which will be the focus of our study it brings together in a harmonious way the subjects of riemannian geometry topology algebraic geometry and symplectic geometry illuminating all these facets of the same object accounts for the length of this paper the self duality equations are equations from gauge theory geometrically they are defined in terms of connections on principal bundles while the group of the principal bundle may be chosen arbitrarily for the equations to make sense we restrict attention here to the simplest case of su 2 or so 3 there are two reasons for this the first and most obvious is that it simplifies calculations and avoids the use of inductive processes which are inherent in the consideration of a general lie group of higher rank the second reason is that solutions for su 2 have an intimate relationship with the internal structure of the riemann surface as a consequence of results we shall prove about solutions to the self duality equations we learn something about the moduli space of complex structures on the surface itself namely teichmuller space

a particle filtering scheme for processing time series corrupted by outliers the literature in engineering and statistics is abounding in techniques for detecting and properly processing anomalous observations in the data most of these techniques have been developed in the framework of static models and it is only in recent years that we have seen attempts that address the presence of outliers in nonlinear time series for a target tracking problem described by a nonlinear state space model we propose the online detection of outliers by including an outlier detection step within the standard particle filtering algorithm the outlier detection step is implemented by a test involving a statistic of the predictive distribution of the observations such as a concentration measure or an extreme upper quantile we also provide asymptotic results about the convergence of the particle approximations of the predictive distribution and its statistics and assess the performance of the resulting algorithms by computer simulations of target tracking problems with signal power observations

tau functions hodge classes and discriminant loci on moduli spaces of hitchin s spectral covers we define two tau functions and on moduli spaces of spectral covers of gl n hitchin systems analyzing the properties of we express the rational divisor class of the universal hitchin s discriminant in terms of standard generators of the rational picard group of the moduli spaces of spectral covers with variable base the function is used to compute the divisor of canonical 1 forms with multiple zeros

adaptive transient solution of nonuniform multiconductor transmission lines using wavelets this paper presents a highly adaptive algorithm for the transient simulation of nonuniform interconnects loaded with arbitrary nonlinear and dynamic terminations the discretization of the governing equations is obtained through a weak formulation using biorthogonal wavelet bases as trial and test functions it is shown how the multiresolution properties of wavelets lead to very sparse approximations of the voltages and currents in typical transient analyzes a simple yet effective time space adaptive algorithm capable of selecting the minimal number of unknowns at each time iteration is described numerical results show the high degree of adaptivity of the proposed scheme

quantum field theory in ninety minutes on presente divers aspects de la theorie quantique des champs la theorie p 2 a 2 dimensions la theorie 3 4 a 3 dimensions la theorie de yang mills a 4 dimensions

decentralized fuzzy control of nonlinear interconnected dynamic delay systems via mixed h_2 h_ infty optimization with smith predictor each subsystem of a nonlinear interconnected dynamic delayed system is approximated by a weighted combination of l transfer function delayed systems tfdss the h2 norm of the difference between the transfer function of a reference model and the closed loop transfer function of the kth tfds of subsystem i is then minimized to obtain a suitable frequency response without incurring oscillating and sluggish phenomena because of the existence of the disturbance at the output of the kth tfds which is not only large but also contains various frequency components the h norm of the weighted sensitivity function between the output disturbance and its corresponding output of the kth tfds is simultaneously minimized to reduce its effect furthermore with proper selection of weighted sensitivity functions certain specific modes of the output disturbance can be eliminated finally two simulations are performed one is the simulation of our designed tfdss with different delays or nonminimum phases and the other is the simulation of an internet based intelligent space for the trajectory tracking of a car like wheeled robot system we demonstrate the effectiveness and efficiency of the proposed control the main contributions of this paper are twofold first the control can simultaneously attain robust performance through the mixed h2 h optimization with a smith predictor and achieve the robust stability via l2n stable with finite gain second fuzzy observer is not needed

the laplacian pyramid as a compact image code we describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions the representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space pixel to pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself the result is a net data compression since the difference or error image has low variance and entropy and the low pass filtered image may represented at reduced sample density further data compression is achieved by quantizing the difference image these steps are then repeated to compress the low pass image iteration of the process at appropriately expanded scales generates a pyramid data structure the encoding process is equivalent to sampling the image with laplacian operators of many scales thus the code tends to enhance salient image features a further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression fast algorithms are described for coding and decoding

interpolating wavelet collocation method of time dependent maxwell s equations characterization of electrically large optical waveguide discontinuities biorthogonal interpolating wavelets have been applied to electromagnetic field modeling through the wavelet collocation method in time domain yielding a versatile first principle algorithm for the solution of time dependent maxwell s equations with inhomogeneous media the resulting scheme maintains high accuracy while by virtue of its sub gridding capability significant reduction of the computational expenditure has been obtained the proposed method has been applied to the analysis of two dimensional dielectric waveguide discontinuities particularly for the modeling of electrically large optical waveguides where the dimension of the analyzed structure is much larger than the wavelength of the highest frequency content of the transmitted signal the proposed method has been proven to be highly efficient compared to the standard finite difference method

ondelettes and phase cell cluster expansions a vindication y meyer has recently developed a particularly useful o n basis forl2 r d expansions using this basis i e expansions into ondelettes or wavelets have yielded important new results in soft and hard analysis the expansion into ondelettes of a boson scalar field naturally leads to phase cell cluster expansions a formalism already developed by the authors using other related bases adoption of ondelettes expansions into the phase cell program gives improvements of some extant results and excises an early error

phase space localization theorem for ondelettes it is proven that an orthonormal basis of ondelettes can never have exponential localization in both position space and momentum space

a quasi armax approach to modelling of non linear systems this paper proposes a class of quasi armax models for non linear systems similar to ordinary non linear armax models the quasi armax models are flexible black box models but they have various linearity properties similar to those of linear armax models a modelling scheme is introduced to construct models consisting of two parts a macro part and a kernel part by using taylor expansion and other mathematical transformation techniques it is first constructed as a class of quasi armax interfaces macro parts that have various linearity properties but contain some complicated coefficients mimo neurofuzzy models kernel parts are then introduced to represent the complicated coefficients it is shown that the proposed quasi armax models have both good approximation ability and some easy to use properties the proposed models have been successfully applied to prediction fault detection and adaptive control of non linear systems

approximate bayesian inference for latent gaussian models by using integrated nested laplace approximations summary structured additive regression models are perhaps the most commonly used class of models in statistical applications it includes among others generalized linear models generalized additive models smoothing spline models state space models semiparametric regression spatial and spatiotemporal models log gaussian cox processes and geostatistical and geoadditive models we consider approximate bayesian inference in a popular subset of structured additive regression models latent gaussian models where the latent field is gaussian controlled by a few hyperparameters and with non gaussian response variables the posterior marginals are not available in closed form owing to the non gaussian response variables for such models markov chain monte carlo methods can be implemented but they are not without problems in terms of both convergence and computational time in some practical applications the extent of these problems is such that markov chain monte carlo sampling is simply not an appropriate tool for routine analysis we show that by using an integrated nested laplace approximation and its simplified version we can directly compute very accurate approximations to the posterior marginals the main benefit of these approximations is computational where markov chain monte carlo algorithms need hours or days to run our approximations provide more precise estimates in seconds or minutes another advantage with our approach is its generality which makes it possible to perform bayesian analysis in an automatic streamlined way and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged

a block spin construction of ondelettes part i lemarie functions using block spin assignments we construct anl2 orthonormal basis consisting of dyadic scalings and translates of just a finite number of functions these functions have exponential localization and for even values of a construction parameterm one can make them classcm 1 with vanishing moments up to orderm inclusive such a basis has an important application to phase cell cluster expansions in quantum field theory

orthonormal bases of compactly supported wavelets we construct orthonormal bases of compactly supported wavelets with arbitrarily high regularity the order of regularity increases linearly with the support width we start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction the construction then follows from a synthesis of these different approaches

simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms this paper examines the applicability of genetic algorithms ga s in the simultaneous design of membership functions and rule sets for fuzzy logic controllers previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions however the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology when ga s have been used to develop both it has been done serially e g design the membership functions and then use them in the design of the rule set this however means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently ga s are fully capable of creating complete fuzzy controllers given the equations of motion of the system eliminating the need for human input in the design loop this new method has been applied to two problems a cart controller and a truck controller beyond the development of these controllers we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules

a suboptimal bootstrap method for structure detection of non linear output error models with application to human ankle dynamics identification of non linear systems involves estimating unknown parameters and structure detection selection of a subset of candidate terms that best describe the observed output this is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design for non linear systems simple output additive noise can generate multiplicative terms between the input output and noise the terms associated with noise need to be modelled to obtain unbiased parameter estimates significantly increasing the number of candidate terms to be estimated and considered in special cases it may be possible to use an output error oe model structure and the instrumental variable iv estimator to obtain unbiased parameters without modelling the noise this significantly reduces the dimensionality of the structure computation problem therefore in this paper we propose a suboptimal bootstrap structure detection sobsd algo

isomonodromic tau function on the space of admissible covers abstract the isomonodromic tau function of the fuchsian differential equations associated to frobenius structures on hurwitz spaces can be viewed as a section of a line bundle on the space of admissible covers we study the asymptotic behavior of the tau function near the boundary of this space and compute its divisor this yields an explicit formula for the pullback of the hodge class to the space of admissible covers in terms of the classes of compactification divisors

lattice structures for optimal design and robust implementation of two channel perfect reconstruction qmf banks a lattice structure and an algorithm are presented for the design of two channel qmf quadrature mirror filter banks satisfying a sufficient condition for perfect reconstruction the structure inherently has the perfect reconstruction property while the algorithm ensures a good stopband attenuation for each of the analysis filters implementations of such lattice structures are robust in the sense that the perfect reconstruction property is preserved in spite of coefficient quantization the lattice structure has the hierarchical property that a higher order perfect reconstruction qmf bank can be obtained from a lower order perfect reconstruction qmf bank simply by adding more lattice sections several numerical examples are provided in the form of design tables

scale free avalanche dynamics in the stock market abstract self organized criticality soc has been claimed to play an important role in many natural and social systems in the present work we empirically investigate the relevance of this theory to stock market dynamics avalanches in stock market indices are identified using a multi scale wavelet filtering analysis designed to remove gaussian noise from the index here new methods are developed to identify the optimal filtering parameters which maximize the noise removal the filtered time series is reconstructed and compared with the original time series a statistical analysis of both high frequency nasdaq e mini futures and daily dow jones data is performed the results of this new analysis confirm earlier results revealing a robust power law behaviour in the probability distribution function of the sizes duration and laminar times between avalanches this power law behaviour holds the potential to be established as a stylized fact of stock market indices in general while the memory process implied by the power law distribution of the laminar times is not consistent with classical models for soc we note that a power law distribution of the laminar times cannot be used to rule out self organized critical behaviour

robust controllability of t s fuzzy model based control systems with parametric uncertainties the robust controllability problem for the takagi sugeno t s fuzzy model based control systems is studied in this paper under the assumption that the nominal t s fuzzy model based control systems are locally controllable i e each fuzzy rule of the nominal t s fuzzy model based control systems has a full row rank for its controllability matrix a sufficient condition is proposed to preserve the assumed property when the parameter uncertainties are added into the nominal t s fuzzy model based control systems the proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties to preserve the assumed property besides a robustly global controllability condition and the related robustly global stabilizability condition of the uncertain t s fuzzy model based control systems are also presented in this paper a nonlinear mass spring damper mechanical system with parameter uncertainties is given as an example to illustrate the application of the proposed sufficient conditions

non linear system identification using hammerstein and non linear feedback models with piecewise linear static maps we consider the identification of hammerstein non linear feedback models by approximating internal non linearities using piecewise linear static maps the resulting method utilizes a point slope parameterization that leads to a computationally tractable optimization problem the computational appeal of this technique is derived from the fact that the method only requires a matrix inverse and singular value decomposition furthermore the identification method simultaneously identifies the linear dynamic and static non linear blocks without requiring prior assumptions on the form of the static non linearity

bayesian non parametric signal extraction for gaussian time series abstract we consider the problem of unobserved components in time series from a bayesian non parametric perspective the identification conditions are treated as unknown and analyzed in a probabilistic framework in particular informative prior distributions force the spectral decomposition to be in an identifiable region then the likelihood function adapts the prior decompositions to the data a full bayesian analysis of unobserved components will be presented for financial high frequency data particularly a three component model long term intra daily and short term will be analyzed to emphasize the importance and the potential of this work when dealing with the value at risk analysis a second astronomical application will show how to deal with multiple periodicities

monte carlo state space likelihoods by weighted posterior kernel density estimation maximum likelihood estimation and likelihood ratio tests for nonlinear non gaussian state space models require numerical integration for likelihood calculations several methods including monte carlo mc expectation maximization mc likelihood ratios direct mc integration and particle filter likelihoods are inefficient for the motivating problem of stage structured population dynamics models in experimental settings an mc kernel likelihood mckl method is presented that estimates classical likelihoods up to a constant by weighted kernel density estimates of bayesian posteriors mckl is derived by using bayesian posteriors as importance sampling densities for unnormalized kernel smoothing integrals mc error and mode bias due to kernel smoothing are discussed and two methods for reducing mode bias are proposed zooming in on the maximum likelihood parameters using a focused prior based on an initial estimate and using a posterior cumulant based approximation of mode bias a simulated example show

conditional parametric models for storm sewer runoff 1 the method of conditional parametric modeling is introduced for flow prediction in a sewage system it is a well known fact that in hydrological modeling the response runoff to input precipitation varies depending on soil moisture and several other factors consequently nonlinear input output models are needed the model formulation described in this paper is similar to the traditional linear models like final impulse response fir and autoregressive exogenous arx except that the parameters vary as a function of some external variables the parameter variation is modeled by local lines using kernels for local linear regression as such the method might be referred to as a nearest neighbor method the results achieved in this study were compared to results from the conventional linear methods fir and arx the increase in the coefficient of determination is substantial furthermore the new approach conserves the mass balance better hence this new approach looks promising for various hydrological models and analysis

wavelet transform based adaptive filters analysis and new results in this paper the wavelet transform is used in an adaptive filtering structure the coefficients of the adaptive filter are updated by the help of the least mean square lms algorithm first the wavelet transform based adaptive filter wtaf is described and it is analyzed for its wiener optimal solution then the performance of the wtaf is studied by the help of learning curves for three different convergence factors 1 constant convergence factor 2 time varying convergence factor and 3 exponentially weighted convergence factor the exponentially weighted convergence factor is proposed to introduce scale based variation to the weight update equation it is shown for two different sets of data that the rate of convergence increases significantly for all three wtaf structures as compared to that of time domain lms the high convergence rates of the wtaf give us reason to expect that it will perform well in tracking rapid changes in a signal

estimating epistemic and aleatory uncertainties during hydrologic modeling an information theoretic approach 1 with growing interest in understanding the magnitudes and sources of uncertainty in hydrological modeling the difficult problem of characterizing model structure adequacy is now attracting considerable attention here we examine this problem via a model structureindependent approach based in information theory in particular we a discuss how to assess and compute the information content in multivariate hydrological data b present practical methods for quantifying the uncertainty and shared information in data while accounting for heteroscedasticity c show how these tools can be used to estimate the best achievable predictive performance of a model for a system given the available data and d show how model adequacy can be characterized in terms of the magnitude and nature of its aleatory uncertainty that cannot be diminished and is resolvable only up to specification of its density and its epistemic uncertainty that can in principle be suitably resolved by improving the model an illustrative modeling example is provided using catchment scale data from three river basins the leaf and chunky river basins in the united states and the chuzhou basin in china our analysis shows that the aleatory uncertainty associated with making catchment simulations using this data set is significant 50 further estimated epistemic uncertainties of the hymod sac sma and xinanjiang model hypotheses indicate that considerable room for model structural improvements remain

explicit equations of motion for constrained mechanical systems with singular mass matrices and applications to multi body dynamics we present the new general explicit form of the equations of motion for constrained mechanical systems applicable to systems with singular mass matrices the systems may have holonomic and or non holonomic constraints which may or may not satisfy d alembert s principle at each instant of time the equation provides new insights into the behaviour of constrained motion and opens up new ways of modelling complex multi body systems examples are provided and applications of the equation to such systems are illustrated

symmetry based scalable lossless compression of 3d medical image data we propose a novel symmetry based technique for scalable lossless compression of 3d medical image data the proposed method employs the 2d integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub bands by exploiting the anatomical symmetries typically present in structural medical images a modified version of the embedded block coder with optimized truncation ebcot tailored according to the characteristics of the data encodes the residual data generated after prediction to provide resolution and quality scalability performance evaluations on a wide range of real 3d medical images show an average improvement of 15 in lossless compression ratios when compared to other state of the art lossless compression methods that also provide resolution and quality scalability including 3d jpeg2000 jpeg2000 and h 264 avc intra coding

numerical methods for the generalized zakharov system we present two numerical methods for the approximation of the generalized zakharov system zs the first one is the time splitting spectral tssp method which is explicit time reversible and time transverse in variant if the generalized zs is keeps the same decay rate of the wave energy as that in the generalized zs gives exact results for the plane wave solution and is of spectral order accuracy in space and second order accuracy in time the second one is to use a local spectral method the discrete singular convolution dsc for spatial derivatives and the fourth order runge kutta rk4 for time integration which is of high the same as spectral order accuracy in space and can be applied to deal with general boundary conditions in order to test accuracy and stability we compare these two methods with other existing methods fourier pseudospectral method fps and wavelet galerkin method wg for spatial derivatives combining with the rk4 for time integration as well as the standard finite difference method fd for solving the zs with a solitary wave solution furthermore extensive numerical tests are presented for plane waves solitary wave collisions in 1d as well as a 2d problem of the generalized zs numerical results show that tssp and dsc are spectralorder accuracy in space and much more accurate than fd and for stability tssp requires k o h dsc rk4 requires k o h2 for fixed acoustic speed where k is the time step and h is the spatial mesh size

generalized dynamic panel data models with random effects for cross section and time abstract an exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non gaussian dynamic panel data model with unobserved random individual specific and time varying effects we propose an estimation procedure based on the importance sampling technique in particular a sequence of conditional importance densities is derived which integrates out all random effects from the joint distribution of endogenous variables we disentangle the integration over both the cross section and the time series dimensions the estimation method facilitates the modeling of large panels in both dimensions we evaluate the method in an extended monte carlo study for dynamic panel data models with observations from different non gaussian distributions we finally present three empirical illustrations for i union choice of young males using a binary panel ii crime rates of families using a binomial panel and iii economic growth modeling using a student s t panel