bootstrap validity for the score test when instruments may be weak it is well known that size adjustments based on bootstrapping the t statistic perform poorly when instruments are weakly correlated with the endogenous explanatory variable in this paper we provide a theoretical proof that guarantees the validity of the bootstrap for the score statistic this theory does not follow from standard results since the score statistic is not a smooth function of sample means and some parameters are not consistently estimable when the instruments are uncorrelated with the explanatory variable

testing for non nested conditional moment restrictions using unconditional empirical likelihood we propose non nested tests for competing conditional moment resctriction models using a method of empirical likelihood our tests are based on the method of conditional empirical likelihood developed by kitamura tripathi and ahn 2004 and zhang and gijbels 2003 by using the conditional implied probabilities we develop three non nested tests the moment encompassing cox type and effcient score encompassing tests compared to the existing non nested tests which mainly focus on testing unconditional moment restrictions our approach directly tests conditional moment restrictions which imply the infinite number of unconditional moment restrictions we derive the null distributions and power properties of the proposed tests simulation experiments show that our tests have reasonable finite sample properties

nonquadratic stabilization of continuous t s fuzzy models lmi solution for a local approach this paper is concerned with nonquadratic stabilization design problem for continuous time nonlinear models in the takagi sugeno t s form obtained by sector nonlinearity approach most of the previous results found in the literature intended to establish global nonquadratic stabilization conditions which are hard to uphold due to the difficulty of handling time derivatives of the membership function by changing the paradigm of global stabilization for something less restrictive a local solution to overcome infeasible quadratic stabilization conditions is offered in this paper it is shown that the derived local nonquadratic conditions actually lead to reasonable advantages over the existing quadratic approach as well as some previous nonquadratic attempts moreover conditions for the solvability of state feedback controller design given here are written in the form of linear matrix inequalities lmis which can be efficiently solved by convex optimization techniques simulation examples are given to demonstrate the validity and applicability of the proposed approaches

approaches to t s fuzzy affine model based reliable output feedback control for nonlinear ito stochastic systems this paper deals with the problem of reliable and robust mathscr h infty static output feedback sof controller synthesis for continuous time nonlinear stochastic systems with actuator faults the nonlinear stochastic plant is expressed by an ito type takagi sugeno fuzzy affine model with parametric uncertainties and a markov process is employed to model the occurrence of actuator fault the purpose is to design an admissible piecewise sof controller such that the resulting closed loop system is stochastically stable with a prescribed disturbance attenuation level in an mathscr h infty sense specifically based on a markovian lyapunov function combined with ito differential formula s procedure and some matrix inequality convexification procedures two new approaches to the reliable sof controller analysis and synthesis are proposed for the underlying stochastic fuzzy affine systems it is shown that the existence of desired reliable controllers is fully characterized in terms of strict linear matrix inequalities finally simulation examples are presented to illustrate the effectiveness and advantages of the developed methods

central limit theorems and uniform laws of large numbers for arrays of random fields over the last decades spatial interaction models have been increasingly used in economics however the development of a sufficiently general asymptotic theory for nonlinear spatial models has been hampered by a lack of relevant central limit theorems clts uniform laws of large numbers ullns and pointwise laws of large numbers llns these limit theorems form the essential building blocks towards developing the asymptotic theory of m estimators including maximum likelihood and generalized method of moments estimators the paper establishes a clt ulln and lln for spatial processes or random fields that should be applicable to a broad range of data processes

non parametric hypothesis testing procedures and applications to demand analysis abstract this paper proposes a test of the hypothesis that a possibly vector valued regression function g lies in a particular compact family of functions f where f is not necessarily a finite dimensional parametric family under the assumption of a known covariance matrix of the errors the test is shown to be consistent for a broad range of alternatives and an upper bound on the significance level is established it is shown how the test may be used to test the utility maximization hypothesis and homotheticity and the paper indicates how to apply the test to a broad range of other non parametric hypotheses

simulated moments estimation of markov models of asset prices this paper provides a simulated moments estimator sme of the parameters of dynamic models in which the state vector follows a time homogeneous markov process conditions are provided for both weak and strong consistency as well as asymptotic normality various tradeoffs among the regularity conditions underlying the large sample properties of the sme are discussed in the context of an asset pricing model this paper provides conditions for the consistency and asymptotic normality of a simulated moments estimator sme of the parameters of asset pricing models with time homogeneous markov representations of the stochastic forc ing process sme s for economic models have been proposed by mcfadden 1989 and pakes and pollard 1989 for i i d environments and by lee and ingram 1991 for a time series environment the sme for time series models examined in this paper is as follows the state vector yt that determines asset prices is assumed to follow a time homogeneous markov process whose transi tion function depends on an unknown parameter vector 3 0 asset prices and possibly other relevant data are observed as f yt 0 for some given function f of the underlying state and parameter vector in parallel a simulated state process y is generated analytically or numerically from the economic model and corresponding simulated observations f yj3 13 are taken for a given parameter choice f3 the parameter is chosen so as to match moments that is to minimize the distance between sample moments of the data f y 8030 and those of the simulated series f yt f3 in a sense to be made precise the proposed sme extends the generalized method of moments gmm estimator hansen 1982 to a large class of asset pricing models for which the moment restrictions of interest do not have analytic representations in terms of observable variables and the unknown parameter vector we provide conditions on the transition function of yt and the observation function f under which the sme of 030 is consistent and characterize the normalized asymptotic distribu tion of the estimator for two reasons neither the regularity conditions underly ing hansen s 1982 analysis of gmm estimators for time series models without

smoothed gmm for quantile models abstract we consider estimation of finite dimensional parameters identified by general conditional quantile restrictions including instrumental variables quantile regression within a generalized method of moments framework moment functions are smoothed to aid both computation and precision consistency and asymptotic normality are established under weaker assumptions than previously seen in the literature allowing dependent data and nonlinear structural models simulations illustrate the finite sample properties an in depth empirical application estimates the consumption euler equation derived from quantile utility maximization advantages of quantile euler equations include robustness to fat tails decoupling risk attitude from the elasticity of intertemporal substitution and error free log linearization

nonparametric identification and estimation of polychotomous choice models abstract in this paper we provide conditions guaranteeing the identification of nonparametric polychotomous choice models in these models neither the subutility function of observable attributes nor the distribution of the unobservable random terms is specified parametrically sets of nonparametric functions that possess properties that are often implied by economic theory and satisfy the restrictions required to identify the models are described we use the identification results to develop nonparametric strongly consistent estimators for the subutility function of observable attributes the results concern models in which the distribution of the unobservable random terms both depend and do not depend on the observable characteristics

lmi based stability analysis for fuzzy model based control systems using artificial t s fuzzy model this paper investigates the stability of fuzzy model based fmb control systems an alternative stability analysis approach using an artificial fuzzy system based on the lyapunov stability theory is proposed to facilitate the stability analysis the continuous membership functions of the takagi sugeno t s fuzzy model are represented by the staircase ones with the nice property of the staircase membership functions it turns the set of infinite number of linear matrix inequality lmi based stability conditions into a finite one furthermore the staircase membership functions carrying system information can be brought to the stability conditions to relax the stability conditions the stability of the original fmb control systems is guaranteed by the satisfaction of the lmi based stability conditions the proposed stability analysis is applied to the fmb control systems of which the t s fuzzy model and fuzzy controller do not share the same premise membership functions and thus is able to enhance the design flexibility of the fuzzy controller a simulation example is given to illustrate the merits of the proposed approach

technical communique reducing conservativeness in recent stability conditions of ts fuzzy systems in this correspondence a new simple strategy for reducing the conservativeness in stability analysis of continuous time takagi sugeno fuzzy systems based on fuzzy lyapunov functions is proposed this new strategy generalizes previous results

testing nonnested euler conditions with quadrature based methods of approximation abstract singleton 1985 proposed a test of the validity of an euler equation specification which incorporates information on a nonnested set of euler equations from an alternative economic model the information about the alternative model is introduced into the testing framework by constructing a sequence of local alternatives to the euler equations of the maintained model which are in the direction of the euler equations of the alternative model singleton s test is easy to apply but is designed to have power against an alternative that in many cases does not have a straightforward economic interpretation in this paper we reexamine the construction of local alternatives to euler equations we compare singleton s approach with a method based on perturbing the data generation process dgp of the maintained model in the direction of the dgp of the alternative model the second approach leads to tests which are always of interest but require the complete specification of the dgp in this paper we derive a test for discriminating between two nonnested sets of euler conditions which have been estimated us ing generalized method of moments gmm the test is based on the encompassing principle of cox 1961 and mizon and richard 1986 and uses tauchen s 1986 quadrature based nethods for approximating the expectation of nonlinear functions of stationary random variables singleton s empirical examples are also reconsidered

new approach to delay dependent stability analysis and stabilization for continuous time fuzzy systems with time varying delay this paper is concerned with delay dependent stability analysis and stabilization problems for continuous time takagi and sugeno t s fuzzy systems with a time varying delay a new method for the delay dependent stability analysis and stabilization is suggested which is less conservative than other existing ones first based on a fuzzy lyapunov krasovskii functional lkf a delay dependent stability criterion is derived for the open loop fuzzy systems in the derivation process some free fuzzy weighting matrices are introduced to express the relationships among the terms of the system equation and among the terms in the leibniz newton formula then a delay dependent stabilization condition based on the so called parallel distributed compensation pdc scheme is worked out for the closed loop fuzzy systems the proposed stability criterion and stabilization condition are represented in terms of linear matrix inequalities lmis and compared with the existing ones via two examples finally application to control of a truck trailer is also given to illustrate the effectiveness of the proposed design method

reliability based code search algorithms for maximum likelihood decoding of block codes efficient code search maximum likelihood decoding algorithms based on reliability information are presented for binary linear block codes the codewords examined are obtained via encoding the information set utilized for encoding comprises the positions of those columns of a generator matrix g of the code which for a given received sequence constitute the most reliable basis for the column space of g substantially reduced computational complexity of decoding is achieved by exploiting the ordering of the positions within this information set the search procedures do not require memory the codeword to be examined is constructed from the previously examined codeword according to a fixed rule consequently the search algorithms are applicable to codes of relatively large size they are also conveniently modifiable to achieve efficient nearly optimum decoding of particularly large codes

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

uniform convergence of vapnik chervonenkis classes under ergodic sampling we show that if x is a complete separable metric space and c is a countable family of borel subsets of x with finite vc dimension then for every stationary ergodic process with values in x the relative frequencies of sets c c converge uniformly to their limiting probabilities beyond ergodicity no assumptions are imposed on the sampling process and no regularity conditions are imposed on the elements of c the result extends existing work of vapnik and chervonenkis among others who have studied uniform convergence for i i d and strongly mixing processes our method of proof is new and direct it does not rely on symmetrization techniques probability inequalities or mixing conditions the uniform convergence of relative frequencies for vc major and vc graph classes of functions under ergodic sampling is established as a corollary of the basic result for sets

generic uniform convergence and equicontinuity concepts for random functions abstract equicontinuity type concepts for random functions which are important for establishing convergence results for such functions have increasingly been used in the econometrics literature in this paper we define and discuss various equicontinuity type concepts for random functions and employ those concepts to provide sufficient conditions for uniform convergence and in particular for uniform laws of large numbers furthermore we clarify the differences and similarities between uniform laws of large numbers based on pointwise and local laws of large numbers given in the recent literature as they relate to differences in the employed equicontinuity type concepts

advanced information of parity bits for decoding short linear block codes using the a algorithm the a algorithm for decoding linear block codes is known to be an efficient tree search method for decoding short linear block codes we propose to employ the early acquired information of some parity bits to speed up the tree search analysis for the number of required information bits for obtaining the parity bits is provided in addition two versions of the modified a algorithm which take advantage of the advanced information of parity bits are provided simulation results show that there is advantage in reducing the number of search tree edges in each proposed version as compared with two existing a algorithms which do not consider the early information of parity bits

on spatial processes and asymptotic inference under near epoch dependence abstract the development of a general inferential theory for nonlinear models with cross sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems to facilitate a general asymptotic inference theory relevant to economic applications this paper first extends the notion of near epoch dependent ned processes used in the time series literature to random fields the class of processes that is ned on say an mixing process is shown to be closed under infinite transformations and thus accommodates models with spatial dynamics this would generally not be the case for the smaller class of mixing processes the paper then derives a central limit theorem and law of large numbers for ned random fields these limit theorems allow for fairly general forms of heterogeneity including asymptotically unbounded moments and accommodate arrays of random fields on unevenly spaced lattices the limit theorems are employed to establish consistency and asymptotic normality of gmm estimators these results provide a basis for inference in a wide range of models with spatial dependence

computationally efficient soft decision decoding of linear block codes based on ordered statistics soft decision decoding of a linear block code using the most reliable basis corresponding to each received word is investigated based either on probabilistic properties or on the structure of the code considered three improvements to the algorithm devised by fossorier and lin see ibid vol 41 no 9 p 1379 1396 1995 are presented these modifications allow large computation savings or significant decoding speedup with little error performance degradation first a reduced probabilistic list of codeword candidates is associated with order i reprocessing of a given code it results in a large reduction of the maximum number of computations with a very small degradation in performance then a probabilistic stopping criterion is introduced for order 0 reprocessing this new test significantly decreases the average number of computations when appropriately implemented finally the application of the algorithm to coset decoding is considered for u u v constructed codes in addition to the conventional coset decoding a new adaptive practically optimum coset decoding method is presented where at each reprocessing stage the number of surviving cosets decreases suboptimum closest coset decoding is also investigated it is shown that two stage decoding with the algorithm of fossorier and lin offers a large variety of choices since the reprocessing order of each stage can be determined independently

deviance information criterion for latent variable models and misspecified models abstract deviance information criterion dic has been widely used for bayesian model comparison especially after markov chain monte carlo mcmc is used to estimate candidate models this paper first studies the problem of using dic to compare latent variable models when dic is calculated from the conditional likelihood in particular it is shown that the conditional likelihood approach undermines theoretical underpinnings of dic a new version of dic namely dic l is proposed to compare latent variable models the large sample properties of dic l are studied a frequentist justification of dic l is provided like aic dic l provides an asymptotically unbiased estimator to the expected kullback leibler kl divergence between the dgp and a predictive distribution some popular algorithms such as the em kalman and particle filtering algorithms are introduced to compute dic l for latent variable models moreover this paper studies the problem of using dic to compare misspecified models a new version of dic namely dic m is proposed and it can be regarded as a bayesian version of tic a frequentist justification of dic m is provided under misspecification dic l and dic m are illustrated using asset pricing models and stochastic volatility models

uniform moment bounds of fisher s information with applications to time series in this paper a uniform over some parameter space moment bound for the inverse of fisher s information matrix is established this result is then applied to develop moment bounds for the normalized least squares estimate in nonlinear stochastic regression models the usefulness of these results is illustrated using time series models in particular an asymptotic expression for the mean squared prediction error of the least squares predictor in autoregressive moving average models is obtained this asymptotic expression provides a solid theoretical foundation for some model selection criteria

admissibility of the likelihood ratio test when a nuisance parameter is present only under the alternative this paper establishes the asymptotic admissibility of the likelihood ratio lr test for a general class of testing problems in which a nuisance parameter is present only under the alternative hypothesis the paper also establishes the finite sample admissibility of the lr test for testing problems of this sort that arise in gaussian linear regression models with known variance

soft decision decoding of linear block codes using preprocessing and diversification order w reprocessing is a suboptimal soft decision decoding approach for binary linear block codes in which up to w bits are systematically flipped on the so called most reliable information basis mrb this correspondence first incorporates two preprocessing rules into order w reprocessing and shows that with appropriate choice of parameters the proposed order w reprocessing with preprocessing requires comparable complexity to order w reprocessing but achieves asymptotically the performance of order w 2 reprocessing to complement the mrb a second basis is employed for practical snrs and this approach is systematically extended to a multibasis order w reprocessing scheme for high signal to noise ratios snrs it is shown that the proposed multibasis scheme significantly enlarges the error correction radius a commonly used measure of performance at high snrs over the original single basis order w reprocessing as a by product this approach also precisely characterizes the asymptotic performance of the well known chase and generalized minimum distance gmd decoding algorithms the proposed algorithm successfully decodes the 192 96 reed solomon concatenated code and the 256 147 extended bch code in near optimal manner within 0 01 db at a block error rate of 10 5 with affordable computational cost

generalized spectral estimation of the consumption based asset pricing model this paper provides a framework for estimating parameters of a wide class of dynamic rational expectations models in the frequency domain the approach is particularly useful for models that are meant to match the data only in limited ways specifically this holds when interest is focused on a subset of frequencies the estimation strategy generalizes band spectrum regression to more general settings and allows for more general loss functions a noteworthy special case involves whitening estimators which force estimated innovations to be close to white noise these estimators can be understood as minimizing a set of moment conditions which grows with sample size and which are typically implied by re models

fuzzy regulators and fuzzy observers relaxed stability conditions and lmi based designs this paper presents new relaxed stability conditions and lmi linear matrix inequality based designs for both continuous and discrete fuzzy control systems they are applied to design problems of fuzzy regulators and fuzzy observers first takagi and sugeno s fuzzy models and some stability results are recalled to design fuzzy regulators and fuzzy observers nonlinear systems are represented by takagi sugeno s ts fuzzy models the concept of parallel distributed compensation is employed to design fuzzy regulators and fuzzy observers from the ts fuzzy models new stability conditions are obtained by relaxing the stability conditions derived in previous papers lmi based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions other lmi s with respect to decay rate and constraints on control input and output are also derived and utilized in the design procedures design examples for nonlinear systems demonstrate the utility of the relaxed stability conditions and the lmi based design procedures

cal h _ infty state feedback control design for fuzzy systems using lyapunov functions with quadratic dependence on fuzzy weighting functions this paper proposes a method for designing an hinfin state feedback fuzzy controller for discrete time takagi sugeno t s fuzzy systems to derive less conservative hinfin stabilization conditions this paper enhances the interactions among the fuzzy subsystems using a multiple lyapunov function with quadratic dependence on fuzzy weighting functions besides for more allocation of the nonlinearity to the fuzzy control system this paper introduces a slack variable that is quadratically dependent on the one step past fuzzy weighting functions as well as the current ones in the derivation the hinfin stabilization conditions are formulated in terms of parameterized linear matrix inequalities plmis which are reconverted into lmi conditions with the help of an efficient relaxation technique

a new lmi based approach to relaxed quadratic stabilization of t s fuzzy control systems this paper proposes a new quadratic stabilization condition for takagi sugeno t s fuzzy control systems the condition is represented in the form of linear matrix inequalities lmis and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature a rigorous theoretic proof is given to show that the proposed condition can include previous results as special cases in comparison with conventional conditions the proposed condition is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design the latter design work is quite hard for t s fuzzy control systems based on the lmi based conditions derived one can easily synthesize controllers for stabilizing t s fuzzy control systems since only a set of lmis is involved the controller design is quite simple and numerically tractable finally the validity and applicability of the proposed approach are successfully demonstrated in the control of a continuous time nonlinear system

a remark on serial correlation in maximum likelihood abstract maximum likelihood estimation can be consistent and asymptotically normal despite serial correlation in the residuals the usual estimator of the asymptotic covariance of the parameter estimator is inconsistent but an alternative consistent estimator is derived

new approaches to h controller designs based on fuzzy observers for t s fuzzy systems via lmi the problems of relaxed quadratic stability conditions fuzzy observer designs and h controller designs for t s fuzzy systems have been studied first new stability conditions are obtained by relaxing the stability conditions derived in previous papers secondly new fuzzy observers based on the relaxed stability conditions for the t s fuzzy systems have been proposed thirdly two sufficient lmi conditions which guarantee the existence of the h controllers based on fuzzy observers for the t s fuzzy systems have been proposed the conditions are not only simple but also consider the interactions among the fuzzy subsystems finally by some examples using the lmi technique we show that the regulators the fuzzy observers and the h controller designs based on new observers for the t s fuzzy systems are very practical and efficient

a new treatment of priority first search maximum likelihood soft decision decoding of linear block codes in this correspondence we present a new method to convert the maximum likelihood soft decision decoding problem for linear block codes into a graph search problem where the generalized dijkstra s algorithm can still be applied to the decoding procedure the cost assigned to every branch in the graph is based on a generalization of the wagner rule which is an equivalent form of the maximum likelihood decoding rule the new decoding algorithm uses the properties of error patterns to reduce the search space

a new fuzzy lyapunov function for relaxed stability condition of continuous time takagi sugeno fuzzy systems this paper presents a new fuzzy lyapunov function flf for the stability analysis of continuous time takagi sugeno t s fuzzy systems unlike conventional flfs the proposed one depends not only on the fuzzy weighting functions of the t s fuzzy systems but on their first order time derivatives as well based on the proposed flf a sufficient stability condition is derived in the form of linear matrix inequalities depending on the upper bounds on the second order time derivative of the fuzzy weighting functions as well as the first order ones finally some examples demonstrate that the proposed condition can provide less conservative results than the previous ones in the literature

decoding linear block codes using a priority first search performance analysis and suboptimal version an efficient maximum likelihood soft decision decoding algorithm for linear block codes using a generalized dijkstra s algorithm was proposed by han hartmann and chen 1993 we prove that this algorithm is efficient for most practical communication systems where the probability of error is less than 10 sup 3 by finding an upper bound of the computational effort of the algorithm a suboptimal decoding algorithm is also proposed the performance of this suboptimal decoding algorithm is within 0 25 db of the performance of an optimal decoding algorithm for the 104 52 binary extended quadratic residue code and within 0 5 db of the optimal performance for the 128 64 binary bch code respectively

stability analysis and design of fuzzy control systems abstract the stability analysis and the design technique of fuzzy control systems using fuzzy block diagrams are discussed first we show the concept of fuzzy blocks and consider the connection problems of fuzzy blocks diagrams we derive some theorems and corollaries with respect to two basic types of connections of fuzzy blocks in order to preserve some properties in a connection of fuzzy blocks continuous piecewise polynomial membership functions are defined secondly a sufficient condition which guarantees the stability of fuzzy systems is obtained in terms of lyapunov s direct method we give an important fact based on this condition thirdly we propose a new design technique of a fuzzy controller the fuzzy block diagrams and the stability analysis are applied to the design problems of a model based fuzzy controller

multi step estimation and forecasting in dynamic models abstract we consider the situation in which a researcher uses a misspecified model for forecasting if the interest is in multi step forecasting and the accuracy of the forecasts is measured through the sum of squared multi step forecast errors then in large samples it is preferable to minimize corresponding sum of squared in sample multi step forecast errors we derive the asymptotic properties of the resulting estimator to asses the behavior of the estimator in small samples we perform a monte carlo experiment in most of the cases considered ols outperforms the estimator we conjecture that this occurs because the estimator is still defined on the basis of squared errors

new approaches to relaxed quadratic stability condition of fuzzy control systems this paper deals with the quadratic stability conditions of fuzzy control systems that relax the existing conditions reported in the previous literatures two new conditions are proposed and shown to be useful in analyzing and designing fuzzy control systems the first one employs the s procedure to utilize information regarding the premise parts of the fuzzy systems the next one enlarges the class of fuzzy control systems whose stability is ensured by representing the interactions among the fuzzy subsystems in a single matrix and solving it by linear matrix inequality the relationships between the suggested stability conditions and the conventional well known stability conditions reported in the previous literatures are also discussed and it is shown in a rigorous manner that the second condition of this paper includes the conventional conditions finally some examples and simulation results are presented to illustrate the effectiveness of the stability conditions

generalized nonquadratic stability of continuous time takagi sugeno models this paper provides generalized nonquadratic stability conditions for continuous time nonlinear models in the takagi sugeno ts form obtained by sector nonlinearity approach should global quadratic stability fail for a given nonlinear model the proposed approach allows the obtaining of progressively better estimations of the stability domain via local asymptotic conditions in the form of linear matrix inequalities lmis which are efficiently solved by convex optimization techniques illustrative examples are presented to emphasize the broadening capabilities of the new stability analysis

a smoothed least squares estimator for threshold regression models abstract we propose a smoothed least squares estimator of the parameters of a threshold regression model our model generalizes that considered in hansen 2000 sample splitting and threshold estimation econometrica 68 575 603 to allow the thresholding to depend on a linear index of observed regressors thus allowing discrete variables to enter we also do not assume that the threshold effect is vanishingly small our estimator is shown to be consistent and asymptotically normal thus facilitating standard inference techniques based on estimated standard errors or standard bootstrap for the slope and threshold parameters

inference for single index quantile regression models with profile optimization single index models offer greater flexibility in data analysis than linear models but retain some of the desirable properties such as the interpretability of the coefficients we consider a pseudo profile likelihood approach to estimation and testing for single index quantile regression models we establish the asymptotic normality of the index coefficient estimator as well as the optimal convergence rate of the nonparametric function estimation moreover we propose a score test for the index coefficient based on the gradient of the pseudo profile likelihood and employ a penalized procedure to perform consistent model selection and model estimation simultaneously we also use monte carlo studies to support our asymptotic results and use an empirical example to illustrate the proposed method

asymptotic theory for differentiated products demand models with many markets abstract this paper develops asymptotic theory for differentiated product demand models with a large number of markets t it takes into account that the predicted market shares are approximated by monte carlo integration with r draws and that the observed market shares are approximated from a sample of n consumers the estimated parameters are t consistent and asymptotically normal as long as r and n grow fast enough relative to t both approximations yield additional bias and variance terms in the asymptotic expansion i propose a bias corrected estimator and a variance adjustment that takes the leading terms into account monte carlo simulations show that these adjustments should be used in applications to avoid severe undercoverage caused by the approximation errors

relaxation technique for a t s fuzzy control design based on a continuous time fuzzy weighting dependent lyapunov function based on the fuzzy weighting dependent lyapunov function this paper proposes a method to drive the relaxed stabilization condition for continuous time takagi sugeno t s fuzzy control systems without resorting to elimination and bounding techniques here to achieve an improved robust h performance while enhancing the interactions among the fuzzy subsystems this paper proposes a valuable relaxation technique that is capable of incorporating the derivatives of fuzzy weighting functions to the relaxation process finally three examples are given to verify the effectiveness of the proposed method

asymptotic behavior of solutions an application to stochastic nlp in this article we study the consistency of optimal and stationary kkt points of a stochastic non linear optimization problem involving expectation functionals when the underlying probability distribution associated with the random variable is weakly approximated by a sequence of random probability measures the optimization model includes constraints with expectation functionals those are not captured in direct application of the previous results on optimality conditions exist in the literature we first study the consistency of stationary points of a general nlp problem with convex and locally lipschitz data and then apply those results to the stochastic nlp problem and stochastic minimax problem moreover we derive an exponential bound for such approximations using a large deviation principle

cross sectional aggregation of non linear models abstract this paper considers the problem of cross sectional aggregation when the underlying micro behavioural relations are characterized by general non linear specifications it focuses on forecasting the aggregates and shows how an optimal aggregate model can be derived by minimizing the mean squared prediction errors conditional on the aggregate information the paper also derives model selection criteria for distinguishing between aggregate and disaggregate models when the primary object of the analysis is forecasting the aggregates and establishes the consistency of the model selection criteria in large samples in the case of standard non linear micro relations with additive errors it also provides suitable small sample corrections for more general non linear specifications we consider bootstrap techniques to correct for small sample bias of the proposed model selection criteria some of the ideas in the paper are illustrated using log linear micro relations often employed in applied research the paper also contains an empirical application where log linear production functions are estimated for the uk economy disaggregated by eight industrial sectors and at the aggregate level over the period 1954 1995

observer based relaxed cal h _ infty control for fuzzy systems using a multiple lyapunov function this short paper proposes a method of designing a fuzzy observer based h infin controller for discrete time takagi sugeno t s fuzzy systems to enhance the applicability of the output feedback controller and improve its performance this short paper first builds a set of fuzzy control rules with premise variables different from those of the t s fuzzy system and sets the overall controller to be dependent on not only the current time but also the one step past information on the estimated fuzzy weighting functions then based on the fuzzy control rules this short paper establishes a less conservative h infin stabilization condition incorporated with a multiple lyapunov function dependent on the estimated fuzzy weighting functions through a two step design procedure the h infin stabilization condition is formulated in terms of parameterized linear matrix equalities plmis which are reconverted into lmis with the help of an efficient and effective relaxation scheme

parameterized linear matrix inequality techniques in fuzzy control system design this paper proposes different parameterized linear matrix inequality plmi characterizations for fuzzy control systems these plmi characterizations are in turn relaxed into pure lmi programs which provides tractable and effective techniques for the design of suboptimal fuzzy control systems the advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations

improved approach to robust cal h _ infty stabilization of discrete time t s fuzzy systems with time varying delays this study is concerned with the robust stabilization problem for a class of discrete time takagi sugeno t s fuzzy systems with time varying delays to improve the conditions for the solvability of the stabilization problem a nonquadratic lyapunov krasovskii functional with a triple summation term is established and a nonparallel distributed compensation non pdc scheme is adopted along with the matrixtransformation technique in addition an appropriate free weightingmatrix method is proposed to exploit novel slack variables for null sum terms especially resulting from i xi 1 xi t xi 1 xi

on estimation and testing when explanatory variables are partly endogenous abstract when some of explanatory variables are correlated with regression errors the standard econometric theory advocates the use of instrumental variables estimators this paper considers a special case where applied researchers do not have a sufficient number of instruments but instead they do have prior information on parameters in the form of parameter restrictions the paper proposes a simple method of estimation and hypothesis testing in the above context the results are extended to restricted gmm estimation in nonlinear context

generalized hamming weights for linear codes motivated by cryptographical applications the algebraic structure of linear codes from a new perspective is studied by viewing the minimum hamming weight as a certain minimum property of one dimensional subcodes a generalized notion of higher dimensional hamming weights is obtained these weights characterize the code performance on the wire tap channel of type ii basic properties of generalized weights are derived the values of these weights for well known classes of codes are determined and lower bounds on code parameters are obtained several open problems are also listed

nonparametric matching and efficient estimators of homothetically separable functions for vectors z and w and scalar v let r v z w be a function that can be non parametrically estimated consistently and asymptotically normally such as a distribution density or conditional mean regression function we provide consistent asymptotically normal nonparametric estimators for the functions g and h where r v z w h vg z w and some related models this framework encompasses homothetic and homothetically separable functions and transformed partly additive models r v z w h v g z w for unknown functions g and h such models reduce the curse of dimensionality provide a natural generalization of linear index models and are widely used in utility production and cost function applications we also provide an estimator of g that is oracle efficient achieving the same performance as an estimator based on local least squares when h is known

a multiple lyapunov function approach to stabilization of fuzzy control systems this paper addresses stability analysis and stabilization for takagi sugeno fuzzy systems via a so called fuzzy lyapunov function which is a multiple lyapunov function the fuzzy lyapunov function is defined by fuzzily blending quadratic lyapunov functions based on the fuzzy lyapunov function approach we give stability conditions for open loop fuzzy systems and stabilization conditions for closed loop fuzzy systems to take full advantage of a fuzzy lyapunov function we propose a new parallel distributed compensation pdc scheme that feedbacks the time derivatives of premise membership functions the new pdc contains the ordinary pdc as a special case a design example illustrates the utility of the fuzzy lyapunov function approach and the new pdc stabilization method

semiparametric least squares sls and weighted sls estimation of single index models abstract for the class of single index models i construct a semiparametric estimator of coefficients up to a multiplicative constant that exhibits 1 n consistency and asymptotic normality this class of models includes censored and truncated tobit models binary choice models and duration models with unobserved individual heterogeneity and random censoring i also investigate a weighting scheme that achieves the semiparametric efficiency bound

strong consistency in nonlinear stochastic regression models the class of nonlinear stochastic regression models includes most of the linear and nonlinear models used in time series stochastic control and stochastic approximation schemes the consistency of least squares estimators was established first by lai we present another set of sufficient conditions for consistency which avoid the use of partial derivatives and are closer in spirit to the conditions presented by wu for non stochastic regression models with independent errors

a systematic approach to improve multiple lyapunov function stability and stabilization conditions for fuzzy systems this paper presents a systematic approach for decreasing conservativeness in stability analysis and control design for takagi sugeno ts systems this approach is based on the idea of multiple lyapunov functions together with simple techniques for introducing slack matrices unlike some previous approaches based on multiple lyapunov functions both the stability and the stabilization conditions are written as linear matrix inequality lmi problems the proposed approach reduces the number of inequalities and guarantees extra degrees of freedom to the lmi problems numeric examples illustrate the effectiveness of this method

dynamic output feedback control for continuous time t s fuzzy systems using fuzzy lyapunov functions a novel relaxation approach is proposed for the analysis and dynamic output feedback control of continuous time takagi sugeno t s fuzzy systems using fuzzy lyapunov functions previous relaxation methods for t s fuzzy systems have some drawbacks in relaxing quadratic functions depending on normalized fuzzy weighting functions they often introduce conservatism or lead to computational difficulty in analysis and control synthesis different from previous works the proposed approach employs linear fractional transformation mechanism and full block s procedure to reduce the conservatism in analysis furthermore the relaxation technique proposed in this paper can be used in solving the controller synthesis problem effectively as a result a design procedure of a nonparallel distributed compensation output feedback controller which ensures asymptotic stability and optimizes mathcal l 2 gain performance of the closed loop systems is provided several examples have been used to illustrate the advantages and efficiency of the proposed method extensively

iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix an iterative algorithm is presented for soft input soft output siso decoding of reed solomon rs codes the proposed iterative algorithm uses the sum product algorithm spa in conjunction with a binary parity check matrix of the rs code the novelty is in reducing a submatrix of the binary parity check matrix that corresponds to less reliable bits to a sparse nature before the spa is applied at each iteration the proposed algorithm can be geometrically interpreted as a two stage gradient descent with an adaptive potential function this adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and therefore significantly improves the performance simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard decision decoding hdd and compare favorably with other popular soft decision decoding methods

semiparametric maximum likelihood estimation of polychotomous and sequential choice models abstract this article considers semiparametric estimation of discrete choice models the estimation method is a semiparametric maximum likelihood method which generalizes klein and spady 1993 to the estimation of polychotomous choice and sequential choice models special emphasis is on the estimation of models with indices of which the density function is not bounded away from zero issues on trimming indices the correction of asymptotic bias and issues of negative kernel density estimation a semiparametric efficiency bound is derived for the polychotomous choice model with index restrictions our estimator is shown to be n consistent asymptotically normal and almost asymptotically efficient monte carlo experiments are provided to evaluate the finite sample performance of the estimator

asymptotics for semiparametric econometric models via stochastic equicontinuity this paper provides a general framework for proving the square root of t consistency and asymptotic normality of a wide variety of semiparametric estimators the class of estimators considered consists of estimators that can be defined as the solution to a minimization problem based on a criterion function that may depend on a preliminary infinite dimensional nuisance parameter estimator the method of proof exploits results concerning the stochastic equicontinuity of stochastic processes the results are applied to the problem of semiparametric weighted least squares estimation of partially parametric regression models primitive conditions are given for square root of t consistency and asymptotic normality of this estimator copyright 1994 by the econometric society

structural change tests for simulated method of moments simulation based estimation methods have become more widely used in recent years we propose a set of tests for structural change in models estimated via simulated method of moments see duffe and singleton econometrica 61 1993 929 these tests extend the work of andrews econometrica 61 1993 821 ghysels et al j econom 82 1997 209 and sowell econometrica 64 1996 1085 tests for violation of moment conditions manuscript graduate school of industrial administration carnegie mellon university which covered generalized method of moments estimators not involving simulation we derive the asymptotic distributions of various tests we show that the number of simulations does not affect the asymptotic distribution under the null but adversely influences local asymptotic power a monte carlo investigation of the finite sample size and power reveals that a relatively small number of simulations suffices to obtain tests with desirable small sample size and power properties

gain tuning of fuzzy pid controllers for mimo systems a performance driven approach a new methodology for tuning the scaling factors or gains of fuzzy proportional integral derivative controllers by taking explicitly into account the closed loop system performance is proposed in this study the solution is obtained by solving a nonlinear constrained optimization problem considering a set of constraints on the scaling factors of the mamdani type fuzzy system and on the plant s inputs and outputs two distinct approaches are presented which are associated with the optimization being carried out offline or in real time the offline tuning scheme assumes the system dynamics described by a nonlinear model while for the real time implementation the plant s dynamics is locally approximated by a linear model with the underlying parameters recursively updated in order to cope with rather stringent sampling time requirements the constrained online optimization problem is implemented based on the grid computing paradigm given the adaptive nature of the real time scheme time varying dynamics and unknown disturbances can be accommodated in such a way that the closed loop performance is effectively maximized while avoiding wind up phenomena induced by the integrator term the proposed tuning methodologies are assessed on a benchmark three tank system and compared against a conventional based tuning approach results from experiments illustrate the feasibility of the proposed approaches and also all the relevance in optimal control systems based on mamdani type fuzzy controllers

semiparametric estimation of count regression models this paper develops a semiparametric estimation approach for mixed count regression models based on series expansion for the unknown density of the unobserved heterogeneity we use the generalized laguerre series expansion around a gamma baseline density to model unobserved heterogeneity in a poisson mixture model we establish the consistency of the estimator and present a computational strategy to implement the proposed estimation techniques in the standard count model as well as in truncated censored and zero inflated count regression models monte carlo evidence shows that the finite sample behavior of the estimator is quite good the paper applies the method to a model of individual shopping behavior

consistent estimation for some nonlinear errors in variables models abstract the issues of identification and estimation of nonlinear errors in variables models are explored the deficiencies of the conventional definition of identification are discussed and an alternative definition in terms of observed samples is suggested the consistency and asymptotic normality of minimum distance estimators are studied to simplify the computation a two step estimation procedure is also suggested in which a consistent estimate of a subset of parameters is first obtained and treated as if they were known in the second step estimation of the rest of the parameters conditions for the consistency of this two step estimator and its asymptotic variance covariance matrix are also derived

an elasticity can be estimated consistently without a priori knowledge of functional form we consider an open question in applied price theory without a priori knowledge of a firm s cost function or a consumer s indirect utility function is it possible to estimate price and substitution elasticities consistently by observing a demand system as the work of white 30 guilkey lovell and sickles 19 and others has shown ordinary flexible functional forms such as the translog cannot achieve this objective we find that if one is prepared to assume that elasticities of substitution cannot oscillate wildly over the region of interest then consistent estimation is possible using the fourier flexible form provided the number of fitted parameters increases as the number of observations increases this result obtains with any of the commonly used statistical methods as for example multivariate least squares maximum likelihood and three stage least squares it obtains if the number of fitted parameters is chosen adaptively by observing the data or chosen deterministically according to some fixed rule we approach the problem along the classical lines of estimability considerations as used in the study of less than full rank linear statistical models and thereby discover that the problem has a fascinating structure which we explore in detail

on a ast algorithms for decoding short linear block codes the a ast algorithm for decoding linear block codes is an efficient tree search method for decoding short linear block codes the information contained in the parity bits are potentially useful for reducing the edges needed in the tree search we propose to extract the information embedded in the parity bits by considering sums of parity bits which can be decided by part of the message bits analysis for the number of required message bits for obtaining sums of parity bits is provided the information contained in the parity bits can help to reduce the number of search tree edges

relaxed stabilization conditions for continuous time takagi sugeno fuzzy control systems this paper deals with the stabilization of continuous time takagi sugeno t s fuzzy control systems based on fuzzy lyapunov functions and nonparallel distributed compensation non pdc control laws new stabilization conditions are represented in the form of linear matrix inequalities lmis the theoretical proof shows that the proposed conditions can provide less conservatism than the existing results in the literature moreover in order to demonstrate the effectiveness of the non pdc control laws the problem of h controller design for t s fuzzy systems is also studied simulation examples are given to illustrate the merits of the proposed methods

a functional version of the birkhoff ergodic theorem for a normal integrand a variational approach in this paper we prove a new version of the birkhoff ergodic theorem bet for random variables depending on a parameter alias integrands this involves variational convergences namely epigraphical hypographical and uniform convergence and requires a suitable definition of the conditional expectation of integrands we also have to establish the measurability of the epigraphical lower and upper limits with respect to the field of invariant subsets from the main result applications to uniform versions of the bet to sequences of random sets and to the strong consistency of estimators are briefly derived

lmi based relaxed nonquadratic stabilization conditions for nonlinear systems in the takagi sugeno s form this paper presents the stabilization analysis for a class of nonlinear systems that are represented by a takagi and sugeno ts discrete fuzzy model takagi and sugeno ieee trans systems man cybern 15 1 1985 116 the main result given here concerns their stabilization using new control laws and new nonquadratic lyapunov functions new relaxed conditions and linear matrix inequality based design are proposed that allow outperforming previous results found in the literature two examples are also provided to demonstrate the efficiency of the approaches

uniform laws of large numbers and stochastic lipschitz continuity this paper presents weak and strong uniform laws of large numbers for stochastically lipschitz continuous averages of functions of independent and dependent random variables it extends results from the literature by making use of the cover number properties of stochastically lipschitz continuous averages on the one hand and convergence speeds of averages of bounded functions on the other hand the resulting conditions for uniform laws of large number for stochastically lipschitz continuous averages of functions trade off heterogeneity and dependence against trending in the lipschitz function of the function class involved

a decoding of block codes the a algorithm is applied to maximum likelihood soft decision decoding of binary linear block codes this paper gives a tutorial on the a algorithm compares the decoding complexity with that of exhaustive search and viterbi decoding algorithms and presents performance curves obtained for several codes

on relaxed lmi based designs for fuzzy regulators and fuzzy observers relaxed conditions for stability of nonlinear continuous and discrete time systems given by fuzzy models are presented a theoretical analysis shows that the proposed methods provide better or at least the same results of the methods presented in the literature numerical results exemplify this fact these results are also used for fuzzy regulators and observers designs the nonlinear systems are represented by fuzzy models proposed by takagi and sugeno 1985 the stability analysis and the design of controllers are described by linear matrix inequalities that can be solved efficiently using convex programming techniques the specification of the decay rate constrains on control input and output are also discussed

relaxed stability conditions for continuous time t s fuzzy control systems via augmented multi indexed matrix approach this paper is concerned with the problem of developing an advanced strategy to reduce the conservatism in stability analysis and control synthesis of continuous time takagi sugeno t s fuzzy systems a novel augmented multi indexed matrix approach is proposed to implement new right hand side slack variables technique for the homogenous polynomial setting combining with the finsler lemma with homogenous matrix lagrange multipliers convergent linear matrix inequality lmi relaxations for stability analysis are proposed by using the generalization of the polya theorem for the case of positive polynomials with matrix valued coefficients a new type of state feedback controller namely the homogeneous polynomially nonquadratic control law hpnqcl is developed to conceive less conservative stabilization conditions the obtained stability and stabilization conditions are further relaxed by using the proposed right hand side slack variables technique moreover the advantages over the existing control schemes are certificated in theory three numerical examples are also provided to illustrate the effectiveness of the proposed methods

predicting binary outcomes we address the issue of using a set of covariates to categorize or predict a binary outcome this is a common problem in many disciplines including economics in the context of a prespecified utility or cost function we examine the construction of forecasts suggesting an extension of the manski 1975 1985 maximum score approach we provide analytical properties of the method and compare it to more common approaches such as forecasts or classifications based on conditional probability models the results are informative for both forecasting environments as well as program allocation where the value of including the participant in the program depends on how useful the program turns out to be for that participant