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Sunday, August 2, 2020 | History

4 edition of Information bounds and nonparametric maximum likelihood estimation found in the catalog.

Information bounds and nonparametric maximum likelihood estimation

by P. Groeneboom

  • 294 Want to read
  • 16 Currently reading

Published by Birkhäuser in Basel, Boston .
Written in English

    Subjects:
  • Factor analysis.,
  • Nonparametric statistics.,
  • Estimation theory.

  • Edition Notes

    Includes bibliographical references (p. [123]-126).

    StatementPiet Groeneboom, Jon A. Wellner.
    SeriesDMV seminar ;, Bd. 19
    ContributionsWellner, Jon A., 1945-
    Classifications
    LC ClassificationsQA278.5 .G76 1992
    The Physical Object
    Paginationviii, 126 p. :
    Number of Pages126
    ID Numbers
    Open LibraryOL1724095M
    ISBN 103764327944, 0817627944
    LC Control Number92027730

    The maximum likelihood set (MLS) is a novel candidate for nonparametric probability estimation from small samples that permits incorporating prior or structural knowledge into the estimator [1]. It is a set of probability distributions which assign to the observed type (or empirical distribution) a likelihood that is no lower than the likelihood they assign to any other type. The MLS has been. This collection of papers delivered at the fifth international Symposium in Economic Theory and Econometrics in is devoted to recent advances in the estimation and testing of models that impose relatively weak restrictions on the stochastic behavior of data. Particularly in highly nonlinear models, empirical results are very sensitive to the choice of the parametric form of the.

    Ebooks list page: ; Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB; Information Bounds and Nonparametric Maximum Likelihood Estimation (Oberwolfach Seminars); Field-Programmable Logic: Architectures, Synthesis and Applications: 4th International Workshop on Field-Programmable Logic and Applications, FPL'94, . Peter Hoff About Research Teaching Book Notes. Most of my articles since are available on arXiv.. A.K. Yanchenko and P.D. Hoff. Hierarchical multidimensional scaling for the comparison of musical performance styles. [ bib | arXiv ] J.G. Bryan and P.D. Hoff. Smaller p-values in genomics studies using distilled historical information. [ bib | arXiv ].

    Linear and nonlinear 2SLS/IV estimation with AR and SAR errors. Limited Information Maximum Likelihood (LIML) and K-class estimation. Wide range of GMM weighting matrix specifications (White, HAC, User-provided) with control over weight matrix iteration. Downloadable (with restrictions)! Author(s): Gallant, A Ronald & Nychka, Douglas W. Abstract: Often maximum likelihood is the method of choice for fitting an econometric model to data but cannot be used because the correct specific ation of (multivariate) density that defines the likelihood is unknown. In this situation, simply put the density equal to a Hermite series and apply standard.


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Information bounds and nonparametric maximum likelihood estimation by P. Groeneboom Download PDF EPUB FB2

This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 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.

Nonparametric Maximum Likelihood Estimation.- 1 The interval censoring problem.- Characterization of the non-parametric maximum likelihood estimators.- Exercises.- 2 The deconvolution. This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 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 by: Get this from a library.

Information bounds and nonparametric maximum likelihood estimation. [P Groeneboom; Jon A Wellner] -- The book gives an account of recent developments in the theory of nonparametric and semiparametric estimation.

The first part deals with information lower bounds and differentiable functionals. The. BOOK 2: Information Bounds and Nonparametric Maximum Likelihood Estimation with Piet Groeneboom; Published by Birkhauser, Boston, Basel, Berlin. Address of Birkhauser: Fifth Avenue, New York, NY Orders and Inquiries: Birkhauser, Dept.

Y, PO BoxSecaucus, NJ Telephone number: Birkhauser web site. Nonparametric Maximum Likelihood Estimation.- 1 The interval censoring problem.- Characterization of the non-parametric maximum likelihood estimators.- Exercises.- 2 The deconvolution problem.- Decreasing densities and non-negative random variables.- Convolution with symmetric densities.- Exercises.- 3 Algorithms.- The EM.

In the case where a parametric model can be reasonably assumed for the underlying survival function, the estimation problem is relatively easy, and the maximum likelihood approach discussed in Section is commonly used for the problem. maximum likelihood deconvolution Download maximum likelihood deconvolution or read online books in PDF, EPUB, Tuebl, and Mobi Format.

Click Download or Read Online button to get maximum likelihood deconvolution book now. This site is like a library, Use search box in. Cambridge Core - Statistical Theory and Methods - Nonparametric Estimation under Shape Constraints - by Piet Groeneboom This book has been cited by the following publications.

Information bounds and nonparametric maximum likelihood estimation. DMV Seminar, vol. Cited by: In particular, our proof simplifies the proof of asymptotic normality of the mean given by P. Groeneboom and J. Wellner [Information bounds and nonparametric maximum likelihood estimation.

Review of the book in Mathematical Reviews of MathSciNet of the American Mathematical Society: review. Annotations and corrections for the book are listed in Annotations for book. The second part of my book "Information Bounds and Nonparametric Maximum Likelihood Estimation" (Birkhäuser, ) with co-author Jon Wellner is based on summer.

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable.

The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both. This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis.

The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is. estimation.

The main results include large deviation bounds for the (quasi) maximum likelihood and the local quadratic bracketing of the log-likelihood process. The latter yields a number of important corollaries for statistical inference: concentration, confidence and risk bounds, expansion of the maximum likelihood estimate, etc.

All these. For example, if is a 95% upper one-sided bound, this would imply that 95% of the population is less is a 95% lower one-sided bound, this would indicate that 95% of the population is greater must be taken to differentiate between one- and two-sided confidence bounds, as these bounds can take on identical values at different percentage levels.

Methods of constructing Uniformly Minimum Variance Unbiased Estimators and Minimum Risk Equivariant Estimators are developed. Lower bounds for the variance of unbiased estimators is derived, which leads to the concept of Fisher-Information. Several methods of estimation, especially the Method of Maximum Likelihood, are introduced.

Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model., Ann.

Statist. 38 – [17] Groeneboom, P. and Jongbloed, G. ()., Nonparametric Estimation under Shape by: 1. that I have found both enlightening and useful: the book on semiparametric informa-tion bounds and nonparametric maximum likelihood estimation by Groeneboom and Wellner (), the review by Huang and Wellner (), the review of current status 1.

Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics. Readers can learn basic ideas and intuitions as well as rigorous treatments of underlying theories and computations from this wonderful book.' Yongdai Kim - Seoul National UniversityCited by: This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation.

It is intended for graduate students in statistics, operations research and applied mathematics, as well as for researchers and practitioners in the field. In statistics, the likelihood function (often simply called the likelihood) expresses how likely particular values of statistical model parameters are for a given sample of data.

It is equal to the joint probability distribution of a random sample, but with the random variable fixed at the given observations.

Its domain therefore is the parameter space of the chosen statistical model, not the.Gill, R. D. () Non-and semi-parametric maximum likelihood estimators and the von Mises method (part II), Scandinavian Journal of Statistics, 20, Grenander, U.

() Abstract inference, Wiley, New York, NY. Groeneboom, P. and Wellner, J. A. () Information bounds and nonparametric maximum likelihood estimation, Birkh~iuser by: 3.Information Bounds and Nonparametric Maximum Likelihood Estimation, Groeneboom P.

and Wellner J.A., Birkhäuser Part II, chaptersand (class 8) Cox Proportional-Hazards Regression for Survival Data, Fox Companion, Fox J.

(download PDF, R code and data set) (class 7) Survival Analysis in R, Diez D. (class 7).