Download or read book Asymptotic Theory in Heteroscedastic Nonlinear Models written by J. Shao and published by . This book was released on 1988 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Theory of Nonlinear Regression written by A.A. Ivanov and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let us assume that an observation Xi is a random variable (r.v.) with values in 1 1 (1R1 , 8 ) and distribution Pi (1R1 is the real line, and 8 is the cr-algebra of its Borel subsets). Let us also assume that the unknown distribution Pi belongs to a 1 certain parametric family {Pi() , () E e}. We call the triple £i = {1R1 , 8 , Pi(), () E e} a statistical experiment generated by the observation Xi. n We shall say that a statistical experiment £n = {lRn, 8 , P; ,() E e} is the product of the statistical experiments £i, i = 1, ... ,n if PO' = P () X ... X P () (IRn 1 n n is the n-dimensional Euclidean space, and 8 is the cr-algebra of its Borel subsets). In this manner the experiment £n is generated by n independent observations X = (X1, ... ,Xn). In this book we study the statistical experiments £n generated by observations of the form j = 1, ... ,n. (0.1) Xj = g(j, (}) + cj, c c In (0.1) g(j, (}) is a non-random function defined on e , where e is the closure in IRq of the open set e ~ IRq, and C j are independent r. v .-s with common distribution function (dJ.) P not depending on ().
Download or read book Asymptotic Theory of Nonlinear Regression written by A. A. Ivanov and published by . This book was released on 2014-01-15 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Structure and Asymptotic Theory for Nonlinear Models with GARCH Errors written by Felix Chan and published by . This book was released on 2010 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Nonlinear Econometric Models written by Benedikt M. Pötscher and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many relationships in economics, and also in other fields, are both dynamic and nonlinear. A major advance in econometrics over the last fifteen years has been the development of a theory of estimation and inference for dy namic nonlinear models. This advance was accompanied by improvements in computer technology that facilitate the practical implementation of such estimation methods. In two articles in Econometric Reviews, i.e., Pötscher and Prucha {1991a,b), we provided -an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature up to the beginning of this decade. Among others, the class of M-estimators contains least mean distance estimators (includ ing maximum likelihood estimators) and generalized method of moment estimators. The present book expands and revises the discussion in those articles. It is geared towards the professional econometrician or statistician. Besides reviewing the literature we also presented in the above men tioned articles a number of then new results. One example is a consis tency result for the case where the identifiable uniqueness condition fails.
Download or read book Asymptotic Theory of Instrumental Variables in Nonlinear Regression written by José Francisco Burguete and published by . This book was released on 1980 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Design of Experiments in Nonlinear Models written by Luc Pronzato and published by Springer Science & Business Media. This book was released on 2013-04-10 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties provides a comprehensive coverage of the various aspects of experimental design for nonlinear models. The book contains original contributions to the theory of optimal experiments that will interest students and researchers in the field. Practitionners motivated by applications will find valuable tools to help them designing their experiments. The first three chapters expose the connections between the asymptotic properties of estimators in parametric models and experimental design, with more emphasis than usual on some particular aspects like the estimation of a nonlinear function of the model parameters, models with heteroscedastic errors, etc. Classical optimality criteria based on those asymptotic properties are then presented thoroughly in a special chapter. Three chapters are dedicated to specific issues raised by nonlinear models. The construction of design criteria derived from non-asymptotic considerations (small-sample situation) is detailed. The connection between design and identifiability/estimability issues is investigated. Several approaches are presented to face the problem caused by the dependence of an optimal design on the value of the parameters to be estimated. A survey of algorithmic methods for the construction of optimal designs is provided.
Download or read book Asymptotic Theory for M estimators in General Autoregressive Conditional Heteroscedasticity Models written by Fabian Tinkl and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Theory for M estimators in General Autoregressive Conditional Heteroscedastic Time Series Models written by Fabian Tinkl and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Weighted Empirical Processes in Dynamic Nonlinear Models written by Hira L. Koul and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unified approach for obtaining the limiting distributions of minimum distance. It discusses classes of goodness-of-t tests for fitting an error distribution in some of these models and/or fitting a regression-autoregressive function without assuming the knowledge of the error distribution. The main tool is the asymptotic equi-continuity of certain basic weighted residual empirical processes in the uniform and L2 metrics.
Download or read book Asymptotic Theory of Statistical Inference for Time Series written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual AR, MA, and ARMA processes. A wide variety of stochastic processes, including non-Gaussian linear processes, long-memory processes, nonlinear processes, non-ergodic processes and diffusion processes are described. The authors discuss estimation and testing theory and many other relevant statistical methods and techniques.
Download or read book Asymptotic Theory in Financial Time Series Models with Conditional Heteroscedasticity written by Theis Lange and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Research Papers in Statistical Inference for Time Series and Related Models written by Yan Liu and published by Springer Nature. This book was released on 2023-05-31 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.
Download or read book ASYMPTOTIC THEORY OF NONLINEAR LEAST SQUARES ESTIMATION written by Chien-Fu WU and published by . This book was released on 1979 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Statistical Modeling written by Takeshi Amemiya and published by Cambridge University Press. This book was released on 2001-01-08 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Download or read book Asymptotic Properties of the Nonparametric Part in Partial Linear Heteroscedastic Regression Models written by Hua Liang and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Asymptotics written by A. R. Brazzale and published by Cambridge University Press. This book was released on 2007-05-31 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: In fields such as biology, medical sciences, sociology, and economics researchers often face the situation where the number of available observations, or the amount of available information, is sufficiently small that approximations based on the normal distribution may be unreliable. Theoretical work over the last quarter-century has led to new likelihood-based methods that lead to very accurate approximations in finite samples, but this work has had limited impact on statistical practice. This book illustrates by means of realistic examples and case studies how to use the new theory, and investigates how and when it makes a difference to the resulting inference. The treatment is oriented towards practice and comes with code in the R language (available from the web) which enables the methods to be applied in a range of situations of interest to practitioners. The analysis includes some comparisons of higher order likelihood inference with bootstrap or Bayesian methods.
