partial non gaussian state space summary in this paper we suggest the use of simulation techniques to extend the applicability of the usual gaussian state space filtering and smoothing techniques to a class of nongaussian time series models this allows a fully bayesian or maximum likelihood analysis of some interesting models including outlier models discrete markov chain components multiplicative models and stochastic variance models finally we discuss at some length the use of a non gaussian model to seasonally adjust the published money supply figures

reducing the number of multipliers in the parallel realization of half band elliptic iir filters it is shown in this correspondence that a halfband iir filter with the poles on the imaginary axis can be derived by the bilinear transformation of an elliptic minimal q factors analog prototype for an odd degree transfer function the residues at the poles are real or imaginary consequently in the parallel realization the number of multipliers can be reduced by half

the conditional autoregressive wishart model for multivariate stock market volatility abstract we propose a conditional autoregressive wishart caw model for the analysis of realized covariance matrices of asset returns our model assumes an autoregressive moving average structure for the scale matrix of the wishart distribution it accounts for positive definiteness of covariance matrices without imposing parametric restrictions and can be estimated by maximum likelihood we also propose extensions of the caw model obtained by including a mixed data sampling midas component and heterogeneous autoregressive har dynamics for long run fluctuations the caw models are applied to realized variances and covariances for five new york stock exchange stocks

marginal likelihood from the metropolis hastings output this article provides a framework for estimating the marginal likelihood for the purpose of bayesian model comparisons the approach extends and completes the method presented in chib 1995 by overcoming the problems associated with the presence of intractable full conditional densities the proposed method is developed in the context of mcmc chains produced by the metropolis hastings algorithm whose building blocks are used both for sampling and marginal likelihood estimation thus economizing on prerun tuning effort and programming experiments involving the logit model for binary data hierarchical random effects model for clustered gaussian data poisson regression model for clustered count data and the multivariate probit model for correlated binary data are used to illustrate the performance and implementation of the method these examples demonstrate that the method is practical and widely applicable

lipschitzian de morgan triplets of fuzzy connectives after studying lipschitzian strong negations lipschitzian de morgan triplets t s n where t is a triangular norm s a triangular conorm and n a strong negation are investigated the relationships between the best lipschitzian constants of functions t s and n are given several examples are included

bayesian computation via the gibbs sampler and related markov chain monte carlo methods the use of the gibbs sampler for bayesian computation is reviewed and illustrated in the context of some canonical examples other markov chain monte carlo simulation methods are also briefly described and comments are made on the advantages of sample based approaches for bayesian inference summaries

a technique for realizing linear phase iir filters a real time iir filter structure is presented that possesses exact phase linearity with 10 approximately 1000 times fewer general multiplies than conventional fir filters of similar performance and better magnitude characteristics than equiripple or maximally flat group delay iir filters this structure is based on a technique using local time reversal and single pass sectioned convolution methods to realized a real time recursive implementation of the noncausal transfer function h z sup 1 the time reversed section technique used to realize exactly linear phase iir filters is described the effects of finite section length on the sectional convolution are analyzed a simulation methodology is developed to address the special requirements of simulating a time reversed section filter a design example is presented with computer simulation to illustrate performance in terms of overall magnitude response and phase linearity as a function of finite section length nine example filter specifications are used to compare the performance and complexity of the time reversed section technique to those of a direct fir implementation

analysis of multifactor affine yield curve models in finance and economics much work has been done on the theoretical modeling and statistical estimation of the yield curve defined as the relationship between 1 log pt and where pt is the time t price of a zero coupon bond with payoff 1 at maturity date t of considerable current interest are models of the yield curve in which a collection of observed and latent factors determine the market price of factor risks the stochastic discount factor and the arbitrage free bond prices the model is particularly interesting from a statistical perspective because the yields are complicated nonlinear functions of the underlying parameters e g those appearing in the evolution dynamics of the factors and those appearing in the model of the factor risks this nonlinearity tends to produce a likelihood function that is multimodal in this article we revisit the question of how such models should be fit from the bayesian viewpoint key aspects of the inferential framework include a a prior on th

a monte carlo approach to nonnormal and nonlinear state space modeling abstract a solution to multivariate state space modeling forecasting and smoothing is discussed we allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation the observational equation or both an adaptive monte carlo integration technique known as the gibbs sampler is proposed as a mechanism for implementing a conceptually and computationally simple solution in such a framework the methodology is a general strategy for obtaining marginal posterior densities of coefficients in the model or of any of the unknown elements of the state space missing data problems including the k step ahead prediction problem also are easily incorporated into this framework we illustrate the broad applicability of our approach with two examples a problem involving nonnormal error distributions in a linear model setting and a one step ahead prediction problem in a situation where both the state and observational equations are nonlinear and involve unknown parameters

stability of weighted penalty based aggregation functions in many practical applications the need arises to aggregate data of varying dimension following from the self identity property some recent studies have looked at the stability of aggregation operators in terms of their behavior as the dimensionality is increased from n 1 to n we use the penalty based representation of aggregation functions in order to investigate the conditions for weighting vectors associated with some important weighted families extending on the results already established for quasi arithmetic means in particular we obtain results for quasi medians and functions that involve a reordering of the inputs such as the owa and order statistics

low power decimator design using approximated linear phase n band iir filter this correspondence presents a new design of a low power decimation filter which consists of a poly phase finite impulse response fir 4 comb filter an approximate linear phase one third band infinite impulse response iir filter and a half band fir filter a poly phase fir 4 comb filter architecture was designed which consumes 75 less power than the recursive comb filter architecture new general equations were derived for the design of 1 n band iir filters and these equations were used to design a novel approximate linear phase one third band iir filter the decimation filter is designed using simulink and dsp blockset and matlab simulations are performed to verify the phase linearity and magnitude response of the designed filter the group delay for the passband region of one third band iir filter has a negligible error of 0 009 the resulting decimation filter has 60 less hardware and consumes 67 less power than the comb fir fir decimation filter

on gibbs sampling for state space models summary we show how to use the gibbs sampler to carry out bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time our approach simultaneously generates the whole of the state vector given the mixture and coefficient indicator variables and simultaneously generates all the indicator variables conditional on the state vectors the states are generated efficiently using the kalman filter we illustrate our approach by several examples and empirically compare its performance to another gibbs sampler where the states are generated one at a time the empirical results suggest that our approach is both practical to implement and dominates the gibbs sampler that generates the states one at a time

phase improvement in iir digital filters a new technique is presented for improving the phase response of finite impulse response iir digital filters in this technique a measure of phase nonlinearity is developed given an iir filter which has the desired amplitude response and it realized as a cascade of second order sections the measure is minimized by making some of the sections anticausal efficient implementation of this algorithm is discussed examples illustrating the technique are also given

bayes inference in regression models with arma p q errors we develop practical and exact methods of analyzing arma p 4 regression error models in a bayesian framework by using the gibbs sampling and metropolis hastings algorithms and we prove that the kernel of the proposed markov chain sampler converges to the true density the procedures can be applied to pure arma time series models and to determine features of the likelihood function by choosing appropriate diffuse priors our results are unconditional on the initial observations we also show how the algorithm can be further simplified for the important special cases of stationary ar p and invertible ma q models recursive transformations developed in this paper to diagonalize the covariance matrix of the errors should prove useful in frequentist estimation examples with simulated and actual economic data are presented kpj ourls gibbs sampling metropolis hastings algorithm data augmentation time series arma processes markov chain bayesian statistics

likelihood based estimation of latent generalized arch structures garch models are commonly used as latent processes in econometrics financial economics and macroeconomics yet no exact likelihood analysis of these models has been provided so far in this paper we outline the issues and suggest a markov chain monte carlo algorithm which allows the calculation of a classical estimator via the simulated em algorithm or a bayesian solution in o t computational operations where t denotes the sample size we assess the performance of our proposed algorithm in the context of both artificial examples and an empirical application to 26 uk sectorial stock returns and compare it to existing approximate solutions

the use of multiple criterion optimization for frequency domain design of noncausal iir filters noncausal recursive filters have recently been proposed because of their increased efficiency versus linear phase fir filters and causal iir filters 2 a general frequency domain design technique capable of exploiting the characteristics of noncausal filters is not yet available this paper proposes the use of multiple criterion optimization for the frequency domain design of noncausal iir filters a new family of filters is generated and their efficiency versus causal fir filters and iir filters is discussed

optimum binary windows for discrete fourier transforms a class of windows called binary windows for the frequency domain implementation of the discrete fourier transform is proposed while the conventional time domain windowing requires n 2 stored values of the data window and n multiplications the binary windows do not require multiplications or stored constants rather they are replaced by shift and add operations typical hardware structures are presented for efficient implementation of the binary windows

lipschitz continuity of copulas w r t lp norms abstract in the framework of the stability analysis of real functions the lipschitz continuity of copulas w r t l p norms is investigated emphasis is put on 1 lipschitz continuity as it is the strongest type possible for copulas after illustrating how to identify 1 lipschitz copulas w r t some l p norm the preservation of this 1 lipschitz continuity by certain construction methods such as ordinal sums and convex sums is demonstrated special attention is given to three important classes of copulas namely archimedean copulas extreme value copulas and archimax copulas

monte carlo smoothing for nonlinear time series we develop methods for performing smoothing computations in general state space models the methods rely on a particle representation of the filtering distributions and their evolution through time using sequential importance sampling and resampling ideas in particular novel techniques are presented for generation of sample realizations of historical state sequences this is carried out in a forward filtering backward smoothing procedure that can be viewed as the nonlinear non gaussian counterpart of standard kalman filter based simulation smoothers in the linear gaussian case convergence in the mean squared error sense of the smoothed trajectories is proved showing the validity of our proposed method the methods are tested in a substantial application for the processing of speech signals represented by a time varying autoregression and parameterized in terms of time varying partial correlation coefficients comparing the results of our algorithm with those from a simple smoother based on the filte

a fast algorithm for signal extraction influence and cross validation in state space models summary a fast algorithm is developed for computing the conditional mean and variance of the signal given the observations in a signal plus noise model the resulting recursions can be applied immediately to provide new and efficient formulae for smoothing part or all of the state vector the ideas of studentized residuals and leverage from regression analysis are generalized to state space models and the new algorithm is used to compute the various measures the results are also applied to obtain a new efficient algorithm for polynomial spline smoothing suppose observations are generated by a gaussian signal plus noise process with the signal described by a state space model such a model for the signal occurs often in practice for example when the signal is the output of a stochastic difference or differential equation this paper presents a new algorithm for signal extraction that is the computa tion of the conditional mean and variance of the signal given the observations the usual method for signal extraction is to use the kalman filter see for example anderson moore 1979 p 105 followed by a smoothing step using an algorithm such as the fixed interval smoothing algorithm anderson moore 1979 p 187 our approach also uses the kalman filter but for the smoothing step introduces a new set of recursions which are more efficient than those for the fixed interval smoothing algorithm as for the fixed interval smoothing algorithm recursions for estimating the signal and its variance are carried out separately so that considerable additional savings can be made if the signal estimate alone is required the development uses ideas introduced by ansley kohn 1987a a remarkable property of the new recursions is that they can be applied immediately

design of multiplierless elliptic iir filters with a small quantization error we present a new technique for the design of multiplierless iir elliptic filters the multiplierless filter has all multiplication constants implemented with a small number of shifters and adders the proposed technique is based on sensitivity analysis an analytical expression for amplitude response sensitivity is derived for the filter structures consisting of two allpass subfilters in parallel it is shown that the amplitude response sensitivity to some constant x can be expressed as a product of the filter reflectance function and the phase sensitivity of the allpass section that implements the constant the closed form expressions for the phase sensitivities of the first and second order allpass sections are also developed it is shown in the paper that the n 1 2 most sensitive constants can be directly controlled by the transfer function parameters if the transfer function is derived by the bilinear transformation from an elliptic minimal q factors emqf analog prototype this way n 1 2 multiplication constants can be implemented without quantization leaving the filter characteristic strictly elliptic this is achieved for a class of low noise allpass sections and for the wave lattice digital filter as well the quantization of the remaining n 1 2 less sensitive constants is performed using the phase tolerance scheme and phase sensitivity functions the proposed design technique is straight forward and consequently very fast the application is demonstrated on the examples of narrowband wideband and halfband filters

analysis of multivariate probit models summary this paper provides a practical simulation based bayesian and non bayesian analysis of correlated binary data using the multivariate probit model the posterior distribution is simulated by markov chain monte carlo methods and maximum likelihood estimates are obtained by a monte carlo version of the em algorithm a practical approach for the computation of bayes factors from the simulation output is also developed the methods are applied to a dataset with a bivariate binary response to a four year longitudinal dataset from the six cities study of the health effects of air pollution and to a sevenvariate binary response dataset on the labour supply of married women from the panel survey of income dynamics

digital filters with equiripple magnitude and group delay this paper introduces a class of recursive digital filters having equiripple behavior in both the magnitude and group delay responses the filters consist of an all pole iir component cascaded with a linear phase fir component the iir component is synthesized so that its group delay exhibits an equiripple variation in the passband whereas the fir component is used to obtain the desired equiripple nature for the amplitude response in both the passband and stopband s an iterative procedure is presented for optimizing the ripple in the group delay and the order of the iir part in such a way that the overall multiplication rate attains its minimum examples show that standard simultaneous amplitude and phase optimization methods only slightly change the results obtained using this method the new method is straightforward to implement and avoids the problems of guessing good initial values and of premature termination several examples show that the new filters require significantly fewer multipliers in narrow band applications than equivalent fir and delay equalized elliptic designs at the expense of a negligible variation in the group delay

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

smoothing and interpolation with the state space model abstract a new result dealing with smoothing and interpolation in the state space model is developed and explored the result simplifies the derivation of existing smoothing algorithms and provides alternate forms that have analytic and practical computing advantages the connection to signal extraction and interpolation is explored and diffuse specifications are considered

a new method for digital all pass filter design a new method for the design of digital all pass filters using chebyshev criterion is introduced it is based on a phase approximation algorithm for polynomial transfer functions the algorithm exploits a scheme of iteratively linearizing the nonlinear constraints in a nonlinear programming and converges theoretically the design method has the advantage that it finds the best uniform phase approximation to an arbitrarily specified phase response without any initial guess of the solution design examples of orders up to 80 obtained on an ibm pc xt personal computer are given to show the practicability of the method

an improved martinet parks algorithm for iir design with unequal numbers of poles and zeros the martinet parks 1978 algorithm for equiripple iir filters with unequal numbers of poles and zeros has been modified to converge for a wider range of designs including an important extraripple case several designs are compared with respect to complexity coefficient sensitivity and group delay the matlab program implementing the algorithm is available by e mail

econometric analysis of realized covariation high frequency based covariance regression and correlation in financial economics this paper analyses multivariate high frequency financial data using realized covariation we provide a new asymptotic distribution theory for standard methods such as regression correlation analysis and covariance it will be based on a fixed interval of time e g a day or week allowing the number of high frequency returns during this period to go to infinity our analysis allows us to study how high frequency correlations regressions and covariances change through time in particular we provide confidence intervals for each of these quantities

sparse bayesian time varying covariance estimation in many dimensions dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality this renders parsimonious estimation methods essential for conducting reliable statistical inference in this paper the issue is addressed by modeling the underlying co volatility dynamics of a time series vector through a lower dimensional collection of latent time varying stochastic factors furthermore we apply a normal gamma prior to the elements of the factor loadings matrix this hierarchical shrinkage prior effectively pulls the factor loadings of unimportant factors towards zero thereby increasing parsimony even more we apply the model to simulated data as well as daily log returns of 300 sp it is implemented in the r package factorstochvol author s abstract

bayesian model discrimination and bayes factors for linear gaussian state space models summary it is shown how to discriminate between different linear gaussian state space models for a given time series by means of a bayesian approach which chooses the model that minimizes the expected loss a practical implementation of this procedure requires a fully bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by markov chain monte carlo methods an application to some non standard situations such as testing hypotheses on the boundary of the parameter space discriminating non nested models and discrimination of more than two models is discussed in detail

the distribution of realized exchange rate volatility using high frequency data on deutschemark and yen returns against the dollar we construct model free estimates of daily exchange rate volatility and correlation that cover an entire decade our estimates termed realized volatilities and correlations are not only model free but also approximately free of measurement error under general conditions which we discuss in detail hence for practical purposes we may treat the exchange rate volatilities and correlations as observed rather than latent we do so and we characterize their joint distribution both unconditionally and conditionally noteworthy results include a simple normality inducing volatility transformation high contemporaneous correlation across volatilities high correlation between correlation and volatilities pronounced and persistent dynamics in volatilities and correlations evidence of long memory dynamics in volatilities and correlations and remarkably precise scaling laws under temporal aggregation

simultaneous design in both magnitude and group delay of iir and fir filters based on multiple criterion optimization this work considers the simultaneous design in both magnitude and group delay of digital transfer functions on the basis of multiple criterion optimization both causal iir filters and nonlinear phase fir filters are studied examples of the optimal tradeoff filters both fir filters and iir filters are presented and their characteristics are analyzed

dynamic linear models with switching abstract the problem of modeling change in a vector time series is studied using a dynamic linear model with measurement matrices that switch according to a time varying independent random process we derive filtered estimators for the usual state vectors and also for the state occupancy probabilities of the underlying nonstationary measurement process a maximum likelihood estimation procedure is given that uses a pseudo expectation maximization algorithm in the initial stages and nonlinear optimization we relate the models to those considered previously in the literature and give an application involving the tracking of multiple targets

linear phase iir filter with equiripple stopband a simple technique of introducing transmission zeros into a low pass all pole filter function to make its stopband transmission equiripple is described the zeros are introduced through a transformation which maps the stopband of the filter onto the unit circle and the passband onto a segment of the real axis in the transformed plane the technique is used to improve the loss characteristic of all pole constant delay filters examples are included to illustrate the design procedure

exact and asymptotic tests for possibly non regular hypotheses on stochastic volatility models abstract we study the problem of testing hypotheses on the parameters of one and two factor stochastic volatility models sv allowing for the possible presence of non regularities such as singular moment conditions and unidentified parameters which can lead to non standard asymptotic distributions we focus on the development of simulation based exact procedures whose level can be controlled in finite samples as well as on large sample procedures which remain valid under non regular conditions we consider wald type score type and likelihood ratio type tests based on a simple moment estimator which can be easily simulated we also propose a c type test which is very easy to implement and exhibits relatively good size and power properties besides usual linear restrictions on the sv model coefficients the problems studied include testing homoskedasticity against a sv alternative which involves singular moment conditions under the null hypothesis and testing the null hypothesis of one factor driving the dynamics of the volatility process against two factors which raises identification difficulties three ways of implementing the tests based on alternative statistics are compared asymptotic critical values when available a local monte carlo or parametric bootstrap test procedure and a maximized monte carlo mmc procedure the size and power properties of the proposed tests are examined in a simulation experiment the results indicate that the c based tests built upon the simple moment estimator available in closed form have good size and power properties for regular hypotheses while monte carlo tests are much more reliable than those based on asymptotic critical values further in cases where the parametric bootstrap appears to fail for example in the presence of identification problems the mmc procedure easily controls the level of the tests moreover mmc based tests exhibit relatively good power performance despite the conservative feature of the procedure finally we present an application to a time series of returns on the standard and poor s composite price index

linear phase low pass iir digital differentiators a novel approach to designing approximately linear phase infinite impulse response iir digital filters in the passband region is introduced the proposed approach yields digital iir filters whose numerators represent linear phase finite impulse response fir filters as an example low pass iir differentiators are introduced the range and high frequency suppression of the proposed low pass differentiators are comparable to those obtained by higher order fir low pass differentiators in addition the differentiators exhibit almost linear phases in the passband regions

filtering via simulation auxiliary particle filters this article analyses the recently suggested particle approach to filtering time series we suggest that the algorithm is not robust to outliers for two reasons the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution here we tackle the first of these problems

marginal likelihood from the gibbs output abstract in the context of bayes estimation via gibbs sampling with or without data augmentation a simple approach is developed for computing the marginal density of the sample data marginal likelihood given parameter draws from the posterior distribution consequently bayes factors for model comparisons can be routinely computed as a by product of the simulation hitherto this calculation has proved extremely challenging our approach exploits the fact that the marginal density can be expressed as the prior times the likelihood function over the posterior density this simple identity holds for any parameter value an estimate of the posterior density is shown to be available if all complete conditional densities used in the gibbs sampler have closed form expressions to improve accuracy the posterior density is estimated at a high density point and the numerical standard error of resulting estimate is derived the ideas are applied to probit regression and finite mixture models

disturbance smoother for state space models summary this paper develops a method to evaluate the smoothed estimator of the disturbance vector in a state space model together with its mean squared error matrix this disturbance smoother also leads to an efficient smoother for the state vector applications include a method to calculate auxiliary residuals for unobserved components time series models and an em algorithm for estimating covariance parameters in a state space model

on the use of aggregation operations in information fusion processes this position paper discusses the role of the existing body of fuzzy set aggregation operations in various kinds of problems where the process of fusion of items coming from several sources is central several kinds of membership functions can be useful according to the nature of the information to be merged numerical vs ordinal inputs preferences vs uncertain data observations vs constraints in each case some aggregation operations look more plausible or feasible than others the aim of this discussion is to suggest directions for putting at work the results of recent mathematical investigations in the structure of aggregation operations

iir digital filters with equiripple stopband transmission recently a simple technique of introducing transmission zeros into a low pass all pole filter transfer function to make its stop band transmission equiripple has been described by hazra 1 it is shown that this technique represents a special case of a more general procedure which provides not only hazra s solution but also additional filters which are advantageous with regard to the required number of multiplications per sample the results are demonstrated with the aid of an example

a new improvement to the powell and chau linear phase iir filters an improvement to the realization of the linear phase iir filters is described it is based on the rearrangement of the numerator polynomials of the iir filter functions that are used in the real time realizations proposed in literature the new realization has better total harmonic distortion when a sine input is used and it has smaller phase and group delay errors due to finite section length

bayes regression with autoregressive errors a gibbs sampling approach abstract this paper develops a practical framework for the bayesian analysis of gaussian and student t regression models with autocorrelated errors as is customary in classical estimation procedures the posteriors are conditioned on the initial observations recourse is taken to the method of gibbs sampling an iterative markovian sampling method and it is shown that the proposed approach can readily deal with high order autoregressive processes without requiring an importance sampling function or other tuning constants several examples including one with ar 4 errors are used to illustrate the ideas

analysis of high dimensional multivariate stochastic volatility models this paper is concerned with the bayesian estimation and comparison of flexible high dimensional multivariate time series models with time varying correlations the model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate stochastic volatility model a unified analysis of the model and its special cases is developed that encompasses estimation filtering and model choice the centerpieces of the estimation algorithm which relies on mcmc methods are 1 a reduced blocking scheme for sampling the free elements of the loading matrix and the factors and 2 a special method for sampling the parameters of the univariate sv process the resulting algorithm is scalable in terms of series and factors and simulation efficient methods for estimating the log likelihood function and the filtered values of the time varying volatilities and correlations are also provided the performance and effectiveness of the inferential methods are extensively tested using simulated data where models up to 50 dimensions and 688 parameters are fit and studied the performance of our model in relation to various multivariate garch models is also evaluated using a real data set of weekly returns on a set of 10 international stock indices we consider the performance along two dimensions the ability to correctly estimate the conditional covariance matrix of future returns and the unconditional and conditional coverage of the 5 and 1 value at risk var measures of four pre defined portfolios

non gaussian ornstein uhlenbeck based models and some of their uses in financial economics non gaussian processes of ornstein uhlenbeck ou type offer the possibility of capturing important distributional deviations from gaussianity and for flexible modelling of dependence structures this paper develops this potential drawing on and extending powerful results from probability theory for applications in statistical analysis their power is illustrated by a sustained application of ou processes within the context of finance and econometrics we construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive ou processes and we study these models in relation to financial data and theory

econometric analysis of realized volatility and its use in estimating stochastic volatility models summary the availability of intraday data on the prices of speculative assets means that we can use quadratic variation like measures of activity in financial markets called realized volatility to study the stochastic properties of returns here under the assumption of a rather general stochastic volatility model we derive the moments and the asymptotic distribution of the realized volatility error the difference between realized volatility and the discretized integrated volatility which we call actual volatility these properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation intensive methods

recursive processing by noncausal digital filters digital filters having exactly linear phase are usually realized by nonrecursive systems however the filter degree and thus the expense becomes rather large in the case of steep filters with high attenuation in the stopband this paper deals with an alternative possibility using noncausal recursive systems after known methods are generalized suitable algorithms are proposed and systematic errors are investigated finally the method is compared with nonrecursive filtering

design of computationally efficient elliptic iir filters with a reduced number of shift and add operations in multipliers in this paper a new design method for elliptic iir filters that provides the implementation of half of the multiplication constants with few shifters and adders is proposed an iir filter when derived by the bilinear transformation from an elliptic minimal q factor analog prototype has its z plane poles on the circle that is orthogonal to the unit circle and has the center on the real axis of the plane due to this property the center of the circle can be used as a parameter for the representation of a pole whereas the second parameter is the radius of the pole it is shown in this paper that the center of the circle is uniquely determined by the frequency for which the filter attenuation is 3 db this result is used for the realization based on the parallel connection of two all pass networks it is shown that all second order all pass sections can be implemented with one common multiplication constant determined by the center of the circle the design method is presented that by an appropriate distribution of a margin in the filter performance predetermines the value of the common constant according to the desired number of shift and add operations this way half of the multipliers are replaced with a limited number of shifters and adders conventional computer programs for iir elliptic digital filters can be used the direct approach for the distribution of the z plane poles among two all pass functions is developed the application and efficiency of the proposed design method are demonstrated by examples

a technique to truncate iir filter impulse response and its application to real time implementation of linear phase iir filters a technique for realizing linear phase infinite impulse response iir filters has been proposed by powell and chau 1991 and gives a real time implementation of h z sup 1 spl middot h z where h z is a causal iir filter function in their system the input signal is divided into l sample sections time reversed section convolved with h z and time reversed again the signal is then filtered by h z to give the system output with a processing delay of 3l 1 samples however the group delay response of the system exhibits a minor sinusoidal variation superimposed on some constant value this variation will degrade image quality in image processing and signal quality in signal transmission applications furthermore the output of the system contains harmonic distortion for a sinusoidal input the main drawbacks of powell and chau s technique are the large processing delay of 3l 1 samples and the accompanying phase and harmonic distortions a smaller processing delay increases the phase and harmonic distortions yet the amplitude response remains acceptable previously the present authors presented a method of reducing the processing delay by shortening the section length by an integer factor n using a structure with increased number of paths for the time reversed signal the authors consider how to reduce the phase and harmonic distortions we examined the operation of the sectioned convolution and analyzed it based on a state space representation then we found that the cause of the distortions is a periodic variation of the impulse response length in the sectioned convolution to overcome this problem a technique is devised to realize a recursive circuit having a truncated impulse response with a fixed length l a system applying this technique to the powell chau system is demonstrated to exhibit perfect linear phase characteristic and produce virtually no harmonic distortion therefore the section length l can be reduced without limitation due to phase and harmonic distortions two methods for reducing the increased computational complexity of this technique assuming fixed l are developed and simulations are performed for the proposed system to confirm the expected improvements

blind deconvolution via sequential imputations abstract the sequential imputation procedure is applied to adaptively and sequentially reconstruct discrete input signals that are blurred by an unknown linear moving average channel and contaminated by additive gaussian noises a problem known as blind deconvolution in digital communication a rejuvenation procedure for improving the efficiency of sequential imputation is introduced and theoretically justified the proposed method does not require the channel to be nonminimum phase and can be used in real time signal restoration two simulated systems are studied to illustrate the proposed method our result shows that the ideas of multiple imputations and flexible simulation techniques are as powerful in engineering as in survey sampling

covariance structure of the gibbs sampler with applications to the comparisons of estimators and augmentation schemes summary we study the covariance structure of a markov chain generated by the gibbs sampler with emphasis on data augmentation when applied to a bayesian missing data problem the gibbs sampler produces two natural approximations for the posterior distribution of the parameter vector the empirical distribution based on the sampled values of the parameter vector and a mixture of complete data posteriors we prove that rao blackwellization causes a one lag delay for the autocovariances among dependent samples obtained from data augmentation and consequently the mixture approximation produces estimates with smaller variances than the empirical approximation the covariance structure results are used to compare different augmentation schemes it is shown that collapsing and grouping random components in a gibbs sampler with two or three components usually result in more efficient sampling schemes

a new approach to the realization of low sensitivity iir digital filters a new implementation of an iir digital filter transfer function is presented that is structurally passive and hence has extremely low pass band sensitivity the structure is based on a simple parallel interconnection of two all pass sections with each section implemented in a structurally lossless manner the structure shares a number of properties in common with wave lattice digital filters computer simulation results verifying the low sensitivity feature are included along with results on roundoff noise dynamic range interaction a large number of alternatives is available for the implementation of the all pass sections giving rise to the well known wave lattice digital filters as a specific instance of the implementation

on lipschitz properties of generated aggregation functions this article discusses lipschitz properties of generated aggregation functions such generated functions include triangular norms and conorms quasi arithmetic means uninorms nullnorms and continuous generated functions with a neutral element the lipschitz property guarantees stability of aggregation operations with respect to input inaccuracies and is important for applications we provide verifiable sufficient conditions to determine when a generated aggregation function holds the k lipschitz property and calculate the lipschitz constants of power means we also establish sufficient conditions which guarantee that a generated aggregation function is not lipschitz we found the only 1 lipschitz generated function with a neutral element email protected 0 1

the simulation smoother for time series models summary recently suggested procedures for simulating from the posterior density of states given a gaussian state space time series are refined and extended we introduce and study the simulation smoother which draws from the multivariate posterior distribution of the disturbances of the model so avoiding the degeneracies inherent in state samplers the technique is important in gibbs sampling with non gaussian time series models and for performing bayesian analysis of gaussian time series

a signal extraction approach to modeling hormone time series with pulses and a changing baseline abstract hormones serve as regulating signals for many biological processes in recent years it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern in addition to the absolute concentration level is important in regulating biological processes consequently it is necessary to characterize the latent secretion patterns from measurements of concentration levels the characterization is complicated by the presence of a biological circadian rhythm when hormone concentrations are plotted over time the resultant time series usually exhibits occasional short rises superimposed on a slowly changing baseline this is a result of a mixture of pulsatile secretions and a circadian rhythm in this article we present a signal extraction approach to model simultaneously a slowly changing component and a pulsatile component of a time series a smoothing spline is used to model the baseline and a multiprocess dynamic linear model is used to model the pulsati

indirect estimation of large conditionally heteroskedastic factor models with an application to the dow 30 stocks we derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a kalman filter approximation with inequality constraints on the auxiliary model parameters we also propose alternative indirect estimators for large scale models and explain how to apply our procedures to many other dynamic latent variable models we analyse the small sample behaviour of our indirect estimators and several likelihood based procedures through an extensive monte carlo experiment with empirically realistic designs finally we apply our procedures to weekly returns on the dow 30 stocks