Sequential Nonparametrics And Semiparametrics
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Nonparametric And Semiparametric Methods In Econometrics And Statistics
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Author : William A. Barnett
language : en
Publisher: Cambridge University Press
Release Date : 1991-06-28
Nonparametric And Semiparametric Methods In Econometrics And Statistics written by William A. Barnett and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-06-28 with Business & Economics categories.
Papers from a 1988 symposium on the estimation and testing of models that impose relatively weak restrictions on the stochastic behaviour of data.
Nonlinear Time Series
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Author : Jiti Gao
language : en
Publisher: CRC Press
Release Date : 2007-03-22
Nonlinear Time Series written by Jiti Gao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-22 with Mathematics categories.
Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
Semiparametric And Nonparametric Methods In Econometrics
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Author : Joel L. Horowitz
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-10
Semiparametric And Nonparametric Methods In Econometrics written by Joel L. Horowitz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-10 with Business & Economics categories.
Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new.
The Oxford Handbook Of Applied Nonparametric And Semiparametric Econometrics And Statistics
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Author : Jeffrey Racine
language : en
Publisher: Oxford University Press
Release Date : 2014-04
The Oxford Handbook Of Applied Nonparametric And Semiparametric Econometrics And Statistics written by Jeffrey Racine and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04 with Business & Economics categories.
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.
Semiparametric Theory And Missing Data
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Author : Anastasios Tsiatis
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-01-15
Semiparametric Theory And Missing Data written by Anastasios Tsiatis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-15 with Mathematics categories.
This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
Statistical Tests Of Nonparametric Hypotheses Asymptotic Theory
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Author : Odile Pons
language : en
Publisher: World Scientific
Release Date : 2013-10-04
Statistical Tests Of Nonparametric Hypotheses Asymptotic Theory written by Odile Pons and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-04 with Mathematics categories.
An overview of the asymptotic theory of optimal nonparametric tests is presented in this book. It covers a wide range of topics: Neyman-Pearson and LeCam's theories of optimal tests, the theories of empirical processes and kernel estimators with extensions of their applications to the asymptotic behavior of tests for distribution functions, densities and curves of the nonparametric models defining the distributions of point processes and diffusions. With many new test statistics developed for smooth curves, the reliance on kernel estimators with bias corrections and the weak convergence of the estimators are useful to prove the asymptotic properties of the tests, extending the coverage to semiparametric models. They include tests built from continuously observed processes and observations with cumulative intervals.
Information Bounds And Nonparametric Maximum Likelihood Estimation
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Author : P. Groeneboom
language : en
Publisher: Birkhäuser
Release Date : 2012-12-06
Information Bounds And Nonparametric Maximum Likelihood Estimation written by P. Groeneboom and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several models are introduced, but not pursued in any detail; many problems remain. Part II, based on Piet Groeneboom's lectures, focuses on non parametric maximum likelihood estimates (NPMLE's) for certain inverse problems. The first chapter deals with the interval censoring problem.
Associated Sequences Demimartingales And Nonparametric Inference
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Author : B.L.S. Prakasa Rao
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-04
Associated Sequences Demimartingales And Nonparametric Inference written by B.L.S. Prakasa Rao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-04 with Mathematics categories.
This book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes. Probabilistic properties of associated sequences, demimartingales and related processes are discussed in the first six chapters. Applications of some of these results to some problems in nonparametric statistical inference for such processes are investigated in the last three chapters.
Applications Of Weak Convergence To Sequential Nonparametric Statistics
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Author : Henry I. Braun
language : en
Publisher:
Release Date : 1974
Applications Of Weak Convergence To Sequential Nonparametric Statistics written by Henry I. Braun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1974 with Order statistics categories.
The two-sample problem is investigated from the sequential nonparametric point of view. (Modified author abstract).
Nonparametric Methods In Sequential Analysis
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Author : Pranab Kumar Sen
language : en
Publisher:
Release Date : 1989
Nonparametric Methods In Sequential Analysis written by Pranab Kumar Sen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Nonparametric statistics categories.