Author: Yuichi Hirose
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 185
Book Description
Efficiency of the Semi-parametric Maximum Likelihood Estimator in Generalized Case-control Studies
Author: Yuichi Hirose
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 185
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 185
Book Description
Information Bounds and Nonparametric Maximum Likelihood Estimation
Author: P. Groeneboom
Publisher: Birkhäuser
ISBN: 3034886217
Category : Mathematics
Languages : en
Pages : 129
Book Description
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.
Publisher: Birkhäuser
ISBN: 3034886217
Category : Mathematics
Languages : en
Pages : 129
Book Description
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.
Semiparametric Maximum Likelihood Estimation
Author: Mingchih Lee
Publisher:
ISBN:
Category : Labor supply
Languages : en
Pages : 200
Book Description
Publisher:
ISBN:
Category : Labor supply
Languages : en
Pages : 200
Book Description
Semiparametric Maximum Likelihood Estimation of GARCH Models
Author: Jian Yang
Publisher: London : Department of Economics, University of Western Ontario
ISBN: 9780771421389
Category :
Languages : en
Pages : 38
Book Description
Publisher: London : Department of Economics, University of Western Ontario
ISBN: 9780771421389
Category :
Languages : en
Pages : 38
Book Description
Semiparametric Maximum Likelihood Estimation of Nonlinear Regression Models and Monte Carlo Evidence
Author: Jian Yang
Publisher: London : Department of Economics, University of Western Ontario
ISBN:
Category : Mathematics
Languages : en
Pages : 68
Book Description
Publisher: London : Department of Economics, University of Western Ontario
ISBN:
Category : Mathematics
Languages : en
Pages : 68
Book Description
Semiparametric Maximum Likelihood Estimation of Nonlinear Regression Models and GARCH Models
Sieve Maximum Likelihood Estimation in a Semi-parametric Regression Model with Errors in Variables
Introduction to Empirical Processes and Semiparametric Inference
Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
ISBN: 0387749780
Category : Mathematics
Languages : en
Pages : 482
Book Description
Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
Publisher: Springer Science & Business Media
ISBN: 0387749780
Category : Mathematics
Languages : en
Pages : 482
Book Description
Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
Semiparametric and Nonparametric Methods in Econometrics
Author: Joel L. Horowitz
Publisher: Springer Science & Business Media
ISBN: 0387928707
Category : Business & Economics
Languages : en
Pages : 278
Book Description
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.
Publisher: Springer Science & Business Media
ISBN: 0387928707
Category : Business & Economics
Languages : en
Pages : 278
Book Description
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.
Semiparametric Maximum Profile Likelihood Estimation of Polytomous and Sequential Choice Models
Author: Lung-Fei Lee
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 35
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 35
Book Description