brief paper the strong stabilization of a one dimensional wave equation by non collocated dynamic boundary feedback control the stabilization of a one dimensional wave equation with non collocated observation at its unstable free end and control at another end is considered the controller comprises a state estimator which is designed in the case where the velocity is not available the method of backstepping is adopted in our design of the feedback law we use the theory of c 0 semigroups and lyapunov functionals to prove the strong stability of the resulting closed loop system

adaptive estimation of regression models via moment restrictions abstract this paper considers adaptive estimation of regression models by means of generalized method of moments estimators two models are considered one with an i i d disturbance that is independent of the regressors and one with a conditionally symmetric but possibly heteroskedastic disturbance for both cases the paper develops linearized estimators that are asymptotically efficient if the number and variety of moment conditions are allowed to grow at an appropriate rate with the sample size in the general symmetric case no other adaptive estimator has yet been proposed also results of a small monte carlo study indicate that in the independence case the small sample performance of the generalized method of moments estimator can be quite good vis a vis other estimators previously proposed

quantiles expectiles and splines abstract a time varying quantile can be fitted by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm it is shown that such quantiles satisfy the defining property of fixed quantiles in having the appropriate number of observations above and below like quantiles time varying expectiles can be estimated by a state space signal extraction algorithm and they satisfy properties that generalize the moment conditions associated with fixed expectiles because the state space form can handle irregularly spaced observations the proposed algorithms can be adapted to provide a viable means of computing spline based non parametric quantile and expectile regressions

m quantile models for small area estimation small area estimation techniques are employed when sample data are insufficient for acceptably precise direct estimation in domains of interest these techniques typically rely on regression models that use both covariates and random effects to explain variation between domains however such models also depend on strong distributional assumptions require a formal specification of the random part of the model and do not easily allow for outlier robust inference we describe a new approach to small area estimation that is based on modelling quantile like parameters of the conditional distribution of the target variable given the covariates this avoids the problems associated with specification of random effects allowing inter domain differences to be characterized by the variation of area specific m quantile coefficients the proposed approach is easily made robust against outlying data values and can be adapted for estimation of a wide range of area specific parameters including that of the quantiles of the distribution of the target variable in the different small areas results from two simulation studies comparing the performance of the m quantile modelling approach with more traditional mixed model approaches are also provided

polynomial nonlinearities in differential equations abstract adomian and his collaborators have found solutions for nonlinear stochastic or deterministic in the limiting case differential equations involving polynomial non linearities using the first author s a n polynomials and recently developed methods of calculating these polynomials it becomes very easy to write solutions for non linear terms such as y m for positive integers m the cases of negative integer m or even decimal powers will appear elsewhere

robust adaptive boundary control of a flexible marine riser with vessel dynamics in this paper robust adaptive boundary control for a flexible marine riser with vessel dynamics is developed to suppress the riser s vibration to provide an accurate and concise representation of the riser s dynamic behavior the flexible marine riser with vessel dynamics is described by a distributed parameter system with a partial differential equation pde and four ordinary differential equations odes boundary control is proposed at the top boundary of the riser based on lyapunov s direct method to regulate the riser s vibration adaptive control is designed when the system parametric uncertainty exists with the proposed robust adaptive boundary control uniform boundedness under the ocean current disturbance can be achieved the proposed control is implementable with actual instrumentation since all the required signals in the control can be measured by sensors or calculated by a backward difference algorithm the state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters simulations are provided to illustrate the applicability and effectiveness of the proposed control

estimation of tail risk based on extreme expectiles we use tail expectiles to estimate value at risk var expected shortfall es and marginal expected shortfall mes three instruments of risk protection of utmost importance in actuarial science and statistical finance the concept of expectiles is a least squares analogue of quantiles both expectiles and quantiles were embedded in the more general class of m quantiles as the minimizers of an asymmetric convex loss function it has been proved very recently that the only m quantiles that are coherent risk measures are the expectiles moreover expectiles define the only coherent risk measure that is also elicitable the elicitability corresponds to the existence of a natural backtesting methodology the estimation of expectiles did not however receive yet any attention from the perspective of extreme values the first estimation method that we propose enables the usage of advanced high quantile and tail index estimators the second method joins together the least asymmetrically weighted squares estimation with the tail restrictions of extreme value theory we establish the limit distributions of the proposed estimators when they are located in the range of the data or near and even beyond the maximum observed loss a main tool is to first estimate the intermediate large expectile based var es and mes and then extrapolate these estimates to the very far tails we show through a detailed simulation study the good performance of the procedures and also present concrete applications to medical insurance data and three large us investment banks

an efficient algorithm for the multivariable adomian polynomials abstract in this article the sum of the series of multivariable adomian polynomials is demonstrated to be identical to a rearrangement of the multivariable taylor expansion of an analytic function of the decomposition series of solutions u 1 u 2 u m about the initial solution components u 1 0 u 2 0 u m 0 of course the multivariable adomian polynomials were developed and are eminently practical for the solution of coupled nonlinear differential equations the index matrices and their simplified forms of the multivariable adomian polynomials are introduced we obtain the recurrence relations for the simplified index matrices which provide a convenient algorithm for rapid generation of the multivariable adomian polynomials another alternative algorithm for term recurrence is established in these algorithms recurrence processes do not require complicated operations such as parametrization expanding and regrouping derivatives etc as practiced in prior art the mathematica program generating the adomian polynomials based on the algorithm in this article is designed

dynamic boundary control of the timoshenko beam abstract we consider a clamped free timoshenko beam to stabilize the beam vibrations we propose a dynamic boundary control law applied at the free end of the beam we prove that with the proposed control law the beam vibrations uniformly and exponentially decay to zero the proof uses a lyapunov functional based on the energy of the system

stabilization and gevrey regularity of a schrodinger equation in boundary feedback with a heat equation we study stability of a schrodinger equation with a collocated boundary feedback compensator in the form of a heat equation with a collocated input output pair remarkably exponential stability is achieved for both positive and negative gains namely as long as the gain is non zero we show that the spectrum of the closed loop system consists only of two branches along two parabolas which are asymptotically symmetric relative to the line re im the 135 line in the second quadrant the asymptotic expressions of both eigenvalues and eigenfunctions are obtained the riesz basis property and exponential stability of the system are then proved finally we show that the semigroup generated by the system operator is of gevrey class 2 a numerical computation is presented for the distributions of the spectrum of the closed loop system

design of distributed h fuzzy controllers with constraint for nonlinear hyperbolic pde systems this paper investigates the problem of designing a distributed h fuzzy controller with constraint for a class of nonlinear spatially distributed processes modeled by first order hyperbolic partial differential equations pdes the purpose of this paper is to design a distributed fuzzy state feedback controller such that the closed loop pde system is exponentially stable with a prescribed h performance of disturbance attenuation while the control constraint is respected initially a takagi sugeno t s hyperbolic pde model is proposed to accurately represent the nonlinear pde system then based on the t s fuzzy pde model a distributed h fuzzy controller design with constraint is developed in terms of a set of coupled differential algebraic linear matrix inequalities d almis in space furthermore a suboptimal distributed h fuzzy controller with constraint is proposed to minimize the level of attenuation the finite difference method in space and the existing linear matrix inequality lmi optimization techniques are employed to approximately solve the suboptimal fuzzy control design problem finally the proposed design method is applied to the distributed control of a nonlinear system described by two coupled first order hyperbolic pdes to illustrate its effectiveness

optimal forecast combinations under general loss functions and forecast error distributions existing results on the properties and performance of forecast combinations have been derived in the context of mean squared error loss under this loss function empirical studies have generally found that equally weighted combined forecasts lead to better performance than estimates of optimal forecast combination weights which in turn outperform the best individual predictions we show that this and other results can be overturned when asymmetries are introduced in the loss function and the forecast error distribution is skewed we characterize the optimal combination weights for the most commonly used alternatives to mean squared error loss and demonstrate how the degree of asymmetry in the loss function and skews in the underlying forecast error distribution can significantly change the optimal combination weights we also propose estimation methods and investigate their small sample properties in simulations and in an inflation forecasting exercise

assessing value at risk with care the conditional autoregressive expectile models in this paper we propose a downside risk measure the expectile based value at risk evar which is more sensitive to the magnitude of extreme losses than the conventional quantile based var qvar the index of an evar is the relative cost of the expected margin shortfall and hence reflects the level of prudentiality it is also shown that a given expectile corresponds to the quantiles with distinct tail probabilities under different distributions thus an evar may be interpreted as a flexible qvar in the sense that its tail probability is determined by the underlying distribution we further consider conditional evar and propose various conditional autoregressive expectile models that can accommodate some stylized facts in financial time series for model estimation we employ the method of asymmetric least squares proposed by newey and powell newey w k powell j l 1987 asymmetric least squares estimation and testing econometrica 55 819 847 and extend their asymptotic results to allow for stationary and weakly dependent data we also derive an encompassing test for non nested expectile models as an illustration we apply the proposed modeling approach to evaluate the evar of stock market indices 2008 elsevier b v all rights reserved

saddlepoint approximations for regression models summary this paper uses the techniques of saddlepoint or tilted exponential approximation to develop an approximation to the small sample distribution of estimators defined by a system of estimating equations when observations are independently but not identically distributed this allows for the explicit treatment of models with explanatory variables the approximation is applied to l1 regression and the accuracy of the approximation is demonstrated in a case in which the usual normal approximation is very poor an analysis of the example indicates the usefulness of a new variant of exponential tilting in calculating the transformation measure a quantity which plays an important role in assuring the accuracy of the approximation

pairwise difference estimators of censored and truncated regression models abstract this paper proposes a class of estimators of the semiparametric censored regression model under the assumption that the error terms are i i d and independent of the covariates the estimators exploit the fact that for a pair of observations a particular transformation of the censored variables depending upon the parameter vector and both covariate vectors will be identically distributed therefore the difference in the transformed dependent variables will be symmetrically distributed around zero when the transformation is evaluated at the true parameter vector an analogous class of estimators for the truncated regression model includes the bhattacharya chernoff and yang 1983 estimator as a special case the estimators are defined as minimizers of u processes so the large sample theory for classical m estimators is extended to this case conditions are given to ensure root n consistency and asymptotic normality of the estimators and a small scale simulation study for the censored regression estimator suggest that it will be well behaved in finite samples

robustifying glejser test of heteroskedasticity verifying that the glejser test for heteroskedasticity is asymptotically invalid unless the error density is symmetric this paper proposes a simple modification to make the test robust to asymmetric disturbances simulation results demonstrate that the size of the modified test is correct for both symmetric and skewed errors

the theoretical foundation of the adomian method abstract in this paper we work out the theoretical basis of the adomian method this powerful method that g adomian has developed in the beginning of the 1980 s supplies analytical approximations to the solution of many kinds of equations thanks to our decomposition theory we justify the practical method and we generalize the convergence results obtained by y cherruault afterward we explain how to solve rigorously equations with linear nonlinear or composed operator and also equations with several terms or with a second member

quantiles for counts this article studies the estimation of conditional quantiles of counts given the discreteness of the data some smoothness must be artificially imposed on the problem we show that it is possible to smooth the data in a way that allows inference to be performed using standard quantile regression techniques the performance and implementation of the estimators are illustrated by simulations and an application

estimation of type 3 tobit models using symmetric trimming and pairwise comparisons abstract this paper considers estimation of the semiparametric type 3 tobit model we construct two step estimators that use the ideas of symmetric trimming and pairwise comparisons which have been proposed for the censored regression model by powell 1986 and honore and powell 1994 the estimators are consistent and asymptotically normal under two identifying assumptions conditional symmetry and independence between the errors and regressors the ideas and techniques are also implementable in other selection models that are variations of the basic type 3 tobit model we demonstrate this by applying the proposed method in estimating an empirical model considered by udry 1994

making and evaluating point forecasts typically point forecasting methods are compared and assessed by means of an error measure or scoring function with the absolute error and the squared error being key examples the individual scores are averaged over forecast cases to result in a summary measure of the predictive performance such as the mean absolute error or the mean squared error i demonstrate that this common practice can lead to grossly misguided inferences unless the scoring function and the forecasting task are carefully matched effective point forecasting requires that the scoring function be specified ex ante or that the forecaster receives a directive in the form of a statistical functional such as the mean or a quantile of the predictive distribution if the scoring function is specified ex ante the forecaster can issue the optimal point forecast namely the bayes rule if the forecaster receives a directive in the form of a functional it is critical that the scoring function be consistent for it in the sense that t

flexible expectile regression in reproducing kernel hilbert spaces abstractexpectile first introduced by newey and powell in 1987 in the econometrics literature has recently become increasingly popular in risk management and capital allocation for financial institutions due to its desirable properties such as coherence and elicitability the current standard tool for expectile regression analysis is the multiple linear expectile regression proposed by newey and powell in 1987 the growing applications of expectile regression motivate us to develop a much more flexible nonparametric multiple expectile regression in a reproducing kernel hilbert space the resulting estimator is called kere which has multiple advantages over the classical multiple linear expectile regression by incorporating nonlinearity nonadditivity and complex interactions in the final estimator the kernel learning theory of kere is established we develop an efficient algorithm inspired by majorization minimization principle for solving the entire solution path of kere it is shown that the algori

an adaptation of adomian decomposition for numeric analytic integration of strongly nonlinear and chaotic oscillators a novel form of an explicit numeric analytic technique is developed for solving strongly nonlinear oscillators of engineering interest the analytic part of this technique makes use of adomian decomposition method adm but unlike other analytical solutions it does not rely on the functional form of the solution over the whole domain of the independent variable instead it discretizes the domain and solves multiple ivps recursively adm uses a rearranged taylor series expansion about a function and finds a series of functions which add up to generate the required solution the present method discretizes the axis of the independent variable and only collects lower powers of the chosen step size in series solution each function constituting the series solution is found analytically it is next shown that the modified adm can be used to obtain the analytical solution in a piecewise form for nonlinear oscillators such a piecewise solution is valid only within a chosen time step an attempt has been made to address few issues like the order of local error and convergence of the method emphasis has been on the application of the present method to a number of well known oscillators the method has the advantage of giving a functional form of the solution within each time interval thus one has access to finer details of the solution over the interval this is not possible in purely numerical techniques like the runge kutta method which provides solution only at the two ends of a given time interval provided that the interval is chosen small enough for convergence it is shown that the present technique successfully overcomes many limitations of the conventional form of adm the present method has the versatility and advantages of numerical methods for being applied directly to highly nonlinear problems and also have the elegance and other benefits of analytical techniques

panel data discrete choice models with lagged dependent variables in this paper we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables lags of the endogenous dependent variable as well as unobservable individual specific effects for the binary logit model with the dependent variable lagged only once chamberlain 1993 gave conditions under which the model is not identified we present a stronger set of conditions under which the parameters of the model are identified the identification result suggests estimators of the model and we show that these are consistent and asymptotically normal although their rate of convergence is slower than the inverse of the square root of the sample size we also consider identification in the semiparametric case where the logit assumption is relaxed we propose an estimator in the spirit of the conditional maximum score estimator manski 1987 and we show that it is consistent in addition we discuss an extension of the identification result to multinomial discrete choice models and to the case where the dependent variable is lagged twice finally we present some monte carlo evidence on the small sample performance of the proposed estimators for the binary response model

controlling for a confounder monotonically related to exposure by means of isotonic quantile regression we present a new approach to evaluating the effect of a continuous exposure factor when that factor increases with a covariate for example age which is regarded as a potential confounder the basic idea is to estimate as functions of the covariate some selected quantiles of the exposure distribution under the assumption that the dependence of each quantile on the covariate is monotonic the resulting estimates are then used to divide the data into different exposure categories this method of categorizing the data implies that the covariate distribution will be almost the same in each exposure group we illustrate the approach with a study of blood pressure and cardiovascular mortality

a test for asymmetry associated with the hodges lehmann estimator abstract one version of the hodges lehmann estimator median xi xj 2 1 i j n is also the minimum distance estimator derived from a particular cramer von mises distance this distance evaluated at that is the minimized distance provides a natural statistic for testing asymmetry of the underlying distribution

boundary control of two dimensional marine risers with bending couplings abstract the aim of this paper is to design a boundary controller for global stabilization of two dimensional marine risers under environmental disturbances based on the energy approach equations of motion including bending couplings for the risers are derived due to the couplings the riser dynamics exhibit mutual effects between transverse motions the lyapunov direct method is used to design the boundary controller proof of the existence and uniqueness of the solutions of the closed loop system is provided based on the galerkin approximation method stability analysis of the closed loop system is carried out using the lyapunov stability theory numerical simulations illustrate the results

new higher order numerical one step methods based on the adomian and the modified decomposition methods abstract we develop new higher order numerical one step methods and apply them to several examples to investigate approximate discrete solutions of nonlinear differential equations these new algorithms are derived from the adomian decomposition method adm and the rach adomian meyers modified decomposition method mdm to present an alternative to such classic schemes as the explicit runge kutta methods for engineering models which require high accuracy with low computational costs for repetitive simulations in contrast to a one size fits all philosophy this new approach incorporates the notion of analytic continuation which extends the region of convergence without resort to other techniques that are also used to accelerate the rate of convergence such as the diagonal pade approximants or the iterated shanks transforms hence global solutions instead of only local solutions are directly realized albeit in a discretized representation we observe that one of the difficulties in applying explicit runge kutta one step methods is that there is no general procedure to generate higher order numeric methods it becomes a time consuming tedious endeavor to generate higher order explicit runge kutta formulas because it is constrained by the traditional picard formalism as used to represent nonlinear differential equations the adm and the mdm rely instead upon adomian s representation and the adomian polynomials to permit a straightforward universal procedure to generate higher order numeric methods at will such as a 12th order or 24th order one step method if need be another key advantage is that we can easily estimate the maximum step size prior to computing data sets representing the discretized solution because we can approximate the radius of convergence from the solution approximants unlike the runge kutta approach with its intrinsic linearization between computed data points we propose new variable step size variable order and variable step size variable order algorithms for automatic step size control to increase the computational efficiency and reduce the computational costs even further for critical engineering models

elicitation complexity of statistical properties a property or statistical functional is said to be elicitable if it minimizes expected loss for some loss function the study of which properties are elicitable sheds light on the capabilities and limitations of point estimation and empirical risk minimization while recent work asks which properties are elicitable we instead advocate for a more nuanced question how many dimensions are required to indirectly elicit a given property this number is called the elicitation complexity of the property we lay the foundation for a general theory of elicitation complexity including several basic results about how elicitation complexity behaves and the complexity of standard properties of interest building on this foundation our main result gives tight complexity bounds for the broad class of bayes risks we apply these results to several properties of interest including variance entropy norms and several classes of financial risk measures we conclude with discussion and open directions

poisson overdispersion estimates based on the method of asymmetric maximum likelihood abstract a common difficulty in regression problems with poisson or binomial or other exponential family response variables is over dispersion the scatter around the fitted regression is too large by the standards of poisson variability this article concerns the description estimation and testing of various patterns of overdispersion with particular emphasis on the poisson case asymmetric maximum likelihood aml is a method of fitting regressions for the conditional percentiles of the response variable as a function of the predictors e g the conditional 90th percentile of y given x distances between the various regression percentiles give a direct assessment of overdispersion the discussion is carried through in terms of an archaeological data set where we see that the counts are overdispersed by a factor of 1 35 in one part of the covariate space but not at all in another moreover the overdispersion is about 40 larger in the positive response direction than in the negative the aml es

d type anticipatory iterative learning control for a class of inhomogeneous heat equations in this paper a d type anticipatory iterative learning control ilc scheme is applied to the boundary control of a class of inhomogeneous heat equations where the heat flux at one side is the control input while the temperature measurement at the other side is the control output by transforming the inhomogeneous heat equation into its integral form and exploiting the properties of the embedded jacobi theta functions the learning convergence of ilc is guaranteed through rigorous analysis without any simplification or discretization of the 3d dynamics in the time space as well as iteration domains the adopted ilc scheme makes full use of the process repetition and deals with state independent or state dependent uncertainties meanwhile due to the feedforward characteristic of ilc the proposed scheme not only makes anticipatory compensation possible to overcome the heat conduction delay in boundary output tracking but also eliminates the gain margin limitation encountered in feedback control in the end an illustrative example is presented to demonstrate the performance of the proposed ilc scheme

a consistent test for conditional symmetry in time series models the assumption of conditional symmetry is often invoked to validate adaptive estimation and consistent estimation of arch garch models by quasi maximum likelihood imposing conditional symmetry can increase the efficiency of bootstraps if the symmetry assumption is valid this paper proposes a procedure for testing conditional symmetry the proposed test does not require the data to be stationary or i i d and the dimension of the conditional variables can be infinite the proposed test is consistent and is asymptotically distribution free in addition the test is shown to have nontrivial power against root t local alternatives the size and power of the test are satisfactory even for small samples applying the test to various time series we reject conditional symmetry in inflation exchange rate and stock returns among the nonfinancial time series considered we find that investment the consumption of durables and manufacturing employment also reject conditional symmetry

optimal stopping under probability distortions in this paper we study optimal stopping problems with respect to distorted expectations with concave distortion functions our starting point is a seminal work of xu and zhou in 2013 who gave an explicit solution of such a stopping problem under a rather large class of distortion functionals in this paper we continue this line of research and prove a novel representation which relates the solution of an optimal stopping problem under distorted expectation to the sequence of standard optimal stopping problems and hence makes the application of the standard dynamic programming based approaches possible furthermore by means of the well known kusuoka representation we extend our results to optimal stopping under general law invariant coherent risk measures finally based on our representations we develop several monte carlo approximation algorithms and illustrate their power for optimal stopping under absolute semideviation risk measures

stabilisation of timoshenko beam system with delay in the boundary control in this paper we consider the exponential stabilisation problem of a timoshenko beam with delay in boundary control suppose that the controller outputs of the forms 1u1 t 1u1 t and 2u2 t 2u2 t where u1 t and u2 t are the inputs of boundary controllers in the past most of the stabilisation results are required j j 0 j 1 2 in the present paper we shall give a new dynamic feedback control law that makes the system exponential stabilisation 0 provided that j j j 1 2 the exponential stabilisation result is proved via test of exact observability of the system

of quantiles and expectiles consistent scoring functions choquet representations and forecast rankings summary in the practice of point prediction it is desirable that forecasters receive a directive in the form of a statistical functional for example forecasters might be asked to report the mean or a quantile of their predictive distributions when evaluating and comparing competing forecasts it is then critical that the scoring function used for these purposes be consistent for the functional at hand in the sense that the expected score is minimized when following the directive we show that any scoring function that is consistent for a quantile or an expectile functional can be represented as a mixture of elementary or extremal scoring functions that form a linearly parameterized family scoring functions for the mean value and probability forecasts of binary events constitute important examples the extremal scoring functions admit appealing economic interpretations of quantiles and expectiles in the context of betting and investment problems the choquet type mixture representations give rise to simple checks of whether a forecast dominates another in the sense that it is preferable under any consistent scoring function in empirical settings it suffices to compare the average scores for only a finite number of extremal elements plots of the average scores with respect to the extremal scoring functions which we call murphy diagrams permit detailed comparisons of the relative merits of competing forecasts

robust adaptive boundary control of an axially moving string under a spatiotemporally varying tension in this paper a vibration suppression scheme for an axially moving string under a spatiotemporally varying tension and an unknown boundary disturbance is investigated the lower bound of the tension variation is assumed to be sufficiently larger than the derivatives of the tension the axially moving string system is divided into two spans i e a controlled span and an uncontrolled span by a hydraulic touch roll actuator which is located in the middle section of the string the transverse vibration of the controlled span part of the string is controlled by the hydraulic touch roll actuator and the position of the actuator is considered as the right boundary of the controlled span part the mathematical model of the system which consists of a hyperbolic partial differential equation describing the dynamics of the moving string and an ordinary differential equation describing the actuator dynamics is derived by using the hamilton s principle the lyapunov method is employed to design a robust boundary control law and adaptation laws for ensuring the vibration reduction of the controlled span part the asymptotic stability of the closed loop system under the robust adaptive boundary control scheme is proved through the use of semigroup theory simulation results verify the effectiveness of the robust adaptive boundary controller proposed

parameter estimation and stabilisation for a one dimensional wave equation with boundary output constant disturbance and non collocated control we are concerned with the parameter estimation and stabilisation of a one dimensional wave equation with an unknown constant disturbance suffered by boundary observation at one end and the non collocated control at another end an adaptive observer is designed in terms of measured velocity with unknown constant disturbance the backstepping method for infinite dimensional system is adopted in the design of the feedback law it is shown that the resulting closed loop system is asymptotically stable meanwhile the estimated parameter is shown to be convergent to the unknown parameter as time goes to infinity

higher order elicitability and osband s principle a statistical functional such as the mean or the median is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score such scoring functions are called strictly consistent for the functional the elicitability of a functional opens the possibility to compare competing forecasts and to rank them in terms of their realized scores in this paper we explore the notion of elicitability for multi dimensional functionals and give both necessary and sufficient conditions for strictly consistent scoring functions we cover the case of functionals with elicitable components but we also show that one dimensional functionals that are not elicitable can be a component of a higher order elicitable functional in the case of the variance this is a known result however an important result of this paper is that spectral risk measures with a spectral measure with finite support are jointly elicitable if one adds the correct quantiles a direct consequence of applied interest is that the pair value at risk expected shortfall is jointly elicitable under mild conditions that are usually fulfilled in risk management applications

parameter estimation and non collocated adaptive stabilization for a wave equation subject to general boundary harmonic disturbance this paper is concerned with the parameter estimation and asymptotic stabilization of a 1 d wave equation that is subject to general harmonic disturbances at the controlled end and suffers from instability at the other end first we design an adaptive observer in terms of measured position and velocity we then adopt the backstepping method for infinite dimensional systems to design an observer based output feedback law the resulting closed loop system is shown to be asymptotically stable and the estimates of the parameters converge to the unknown parameters

absolute stability of the kirchhoff string with sector boundary control this paper addresses the stability problem of the nonlinear kirchhoff string with nonlinear boundary control the nonlinear boundary control is the negative feedback of the transverse velocity of the string at one end which satisfies a sector constraint employing the integral type multiplier method we establish explicit absolute exponential stability of the kirchhoff string system

bias corrected score decomposition for generalized quantiles summarydecompositions of the score of a forecast represent useful tools for assessing its performance we consider local score decompositions permitting detailed forecast assessments across a spectrum of conditions of interest we derive corrections to the bias of the decomposition components in the framework of point forecasts of quantile type functionals and illustrate their performance by simulation related bias corrections have thus far only been known for squared error criteria

stochastic boundary control design for timoshenko beams with large motions abstract this paper considers modeling and boundary control of timoshenko beams with large motions under both deterministic and stochastic external loads the original nonlinear partial differential equations governing motion of the beams are derived and used in the control design the control design is based on the lyapunov direct method the proposed controllers guarantee globally practically k exponentially p stability of the beam motions at the reference state well posedness and stability are analyzed based on a lyapunov type theorem developed to study well posedness and stability for a class of stochastic evolution systems in hilbert space simulation results are included to illustrate the effectiveness of the proposed control design

boundary control of a timoshenko beam system with input dead zone in this paper boundary control is designed for a timoshenko beam system with the input dead zone by the hamilton s principle the dynamics of the timoshenko beam system is represented by a distributed parameter model with two partial differential equations and four ordinary differential equations the bounded part is separated from the input dead zone and then forms the disturbance like term together with the boundary disturbance which finally acts on the timoshenko beam system boundary control based on the lyapunov s direct method is proposed to ensure the timoshenko beam converge into a small neighbourhood of zero where stability of the system is also analysed besides the existence and uniqueness of the solution of the timoshenko beam system are proved simulations are provided to reveal the applicability and effectiveness of the proposed control scheme

zonoid trimming for multivariate distributions a family of trimmed regions is introduced for a probability distribution in euclidean d space the regions decrease with their parameter a from the closed convex hull of support at a 0 to the expectation vector at 1 the family determines the underlying distribution uniquely for every the region is affine equivariant and continuous with respect to weak convergence of distributions the behavior under mixture and dilation is studied a new concept of data depth is introduced and investigated finally a trimming transform is constructed that injectively maps a given distribution to a distribution having a unique median

stabilization of exact nonlinear timoshenko beams in space by boundary feedback abstract boundary feedback controllers are designed to stabilize timoshenko beams with large translational and rotational motions in space under external disturbances the exact nonlinear partial differential equations governing motion of the beams are derived and used in the control design the designed controllers guarantee globally practically asymptotically and locally practically exponentially stability of the beam motions at the reference state the control design well posedness and stability analysis are based on various relationships between the earth fixed and body fixed coordinates sobolev embeddings and a lyapunov type theorem developed to study well posedness and stability for a class of evolution systems in hilbert space simulation results are included to illustrate the effectiveness of the proposed control design

trilateral teleoperation of adaptive fuzzy force motion control for nonlinear teleoperators with communication random delays in this paper an adaptive fuzzy control scheme is proposed for hybrid motion force of trilateral teleoperation systems with a dual master single slave configuration under stochastic time varying delays in communication channels different from previous works on bilateral teleoperation systems this paper addresses dual master trilateral control of a single holonomic constrained robotic manipulator where the communication delays are modeled as multiple markov chains and the motion force controls are investigated under consideration of unsymmetric stochastic time varying delays and system dynamical uncertainties using partial feedback linearization the whole trilateral teleoperation system which consists of both master and slave manipulator dynamics is transformed into three subsystems by integrating markov jump systems to handle random delays adaptive fuzzy control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems flss it is proven that the trilateral teleoperation system is stochastically stable in mean square under specific linear matrix inequality lmi conditions and all the signals of the resulting closed loop system are uniformly bounded the proposed scheme is validated by extensive simulations

simulation based finite sample tests for heteroskedasticity and arch effects tests for heteroskedasticity in linear regressions are typically based on asymptotic approximations we show that the size of such tests can be perfectly controlledin nite samples through monte carlo test techniques with both gaussian and non gaussian heavy tailed disturbance distributions the procedures studied include standard heteroskedasticity tests e g glejser bartlett cochran hartley breusch pagan godfrey white szroeter as well as tests for arch type heterosked asticity sup type andcombinedtests are also proposedto allow for unknown breakpoints in the variance the fact that the proposedproced ures achieve size control andhave goodpower is demonstrated in a monte carlo simulation c 2003 elsevier b v all rights reserved

estimation of best predictors of binary response abstract this paper investigates analog estimation of predictors of binary response given semiparametric restrictions on the response process results are presented for the isotonic response model and for the weaker single crossing model the isotonic model assumes that the response probability is monotone in a linear function of the conditioning variables in the single crossing model the response probability is above a given value when a linear function of the conditioning variables is positive and below that value otherwise it is found that analog estimators computed under the isotonicity restriction are generally consistent estimation under the single crossing restriction is consistent only for special loss functions

on m estimators and normal quantiles this paper explores a class of robust estimators of normal quantiles filling the gap between maximum likelihood estimators and empirical quantiles our estimators are linear combinations of m estimators their asymptotic variances can be arbitrarily close to variances of the maximum likelihood estimators compared with empirical quantiles the new estimators offer considerable reduction of variance at near normal probability distributions

nonlinear stochastic differential delay equations abstract the methods used by adomian and his co workers to solve linear and nonlinear stochastic differential equations will be demonstrated to be applicable to differential equations deterministic or stochastic involving delays constant time dependent or random

glejser s test revisited godfrey 1996 journal of econometrics 72 275 299 has shown that the glejser test for heteroskedasticity is valid only under conditional symmetry here modifications of the glejser test are suggested the proposed test statistics are asymptotically valid even when the disturbances are not symmetrically distributed and can be used to test for heteroskedasticity when conditional location functions other than the conditional mean are estimated

asymmetric least squares estimation and testing this paper considers estimation and hypothesis tests for coefficients of linear regression models where the coefficient estimates are based on location measures defined by an asymmetric least squares criterion function these asymmetric least squares estimators have properties which are analogous to regression quantile estimators but are much simpler to calculate as are the corresponding test statistics the coefficient estimators can be used to construct test statistics for homoskedasticity and conditional symmetry of the error distribution and we find these tests compare quite favorably with other commonly used tests of these null hypotheses in terms of local relative efficiency consequently asymmetric least squares estimation provides a convenient and relatively efficient method of summarizing the conditional distribution of a dependent variable given the regressors and a means of testing whether a linear model is an adequate characterization of the typical value for this conditional distribution

a simple consistent test of conditional symmetry in symmetrically trimmed tobit models we propose a weighted and sample size adjusted kolmogorov smirnov type statistic to test the assumption of conditional symmetry maintained in the symmetrically trimmed least squares stls approach of powell 1986b which is widely used to estimate censored or truncated regression models without making distributional assumptions our statistic is consistent and does not require any nonparametric smoothing although we test the validity of a conditional feature we also propose a bootstrap procedure to obtain the p values and critical values that are required to carry out the test in practical applications results from a simulation study suggest that our test can work very well even in small to moderately sized samples as an empirical illustration we apply our test to two datasets that have been used in the literature to estimate censored regression models using powell s stls approach to check whether the assumption of conditional symmetry is supported by these datasets

boundary control of transverse motion of marine risers with actuator dynamics this paper presents a boundary controller to reduce transverse motion of flexible marine risers driven by a hydraulic system at the top end of the risers under environmental disturbances induced by waves wind and ocean currents the boundary controller is designed based on lyapunov s direct method and the backstepping technique proof of existence and uniqueness of the solutions of the closed loop control system is carried out by using the galerkin approximation method simulation results illustrate the effectiveness of the proposed boundary controller

trimmed lad and least squares estimation of truncated and censored regression models with fixed effects this paper considers estimation of truncated and censored regression models with fixed effects up until now no estimator has been shown to be consistent as the cross section dimension increases with the time dimension fixed trimmed least absolute deviations and trimmed least squares estimators are proposed for the case where the panel is of length two and it is proven that they are consistent and asymptotically normal it is not necessary to maintain parametric assumptions on the error terms to obtain this result a small scale monte carlo study demonstrates that these estimators can perform well in small samples copyright 1992 by the econometric society