A Finite Sample Optimality Property of Nonparametric Maximum Likelihood Estimator PDF Download
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Author: Chin Chin Ong
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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Book Description
Author: Chin Chin Ong
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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Book Description
Author: Nancy Milena Hoyos Gomez
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Michel Talagrand
Publisher: Springer Science & Business Media
ISBN: 3540274995
Category : Mathematics
Languages : en
Pages : 227
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Book Description
The fundamental question of characterizing continuity and boundedness of Gaussian processes goes back to Kolmogorov. After contributions by R. Dudley and X. Fernique, it was solved by the author. This book provides an overview of "generic chaining", a completely natural variation on the ideas of Kolmogorov. It takes the reader from the first principles to the edge of current knowledge and to the open problems that remain in this domain.
Author: P.P.B. Eggermont
Publisher: Springer Nature
ISBN: 1071612441
Category : Mathematics
Languages : en
Pages : 514
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Book Description
This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Author: B. L. S. Prakasa Rao
Publisher: Academic Press
ISBN: 148326923X
Category : Mathematics
Languages : en
Pages : 539
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Book Description
Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both for specialists in the area as well as for students of statistics taking courses at the postgraduate level. The main emphasis throughout the book is on the discussion of several methods of estimation and on the study of their large sample properties. Chapters are devoted to topics on estimation of density and related functions, the application of density estimation to classification problems, and the different facets of estimation of distribution functions. Statisticians and students of statistics and engineering will find the text very useful.
Author: Alex McMillan
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 262
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Author: Zacharias Psaradakis
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 0
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Author: Francis X. Diebold
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 38
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Book Description
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.
Author: Zacharias G. Psaradakis
Publisher:
ISBN:
Category :
Languages : en
Pages : 15
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Book Description
Author: Alexandre B. Tsybakov
Publisher: Springer Science & Business Media
ISBN: 0387790527
Category : Mathematics
Languages : en
Pages : 222
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Book Description
Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.
