Peter J. Bickel, Kjell A. Doksum

Mathematical Statistics (eBook, ePUB)

Basic Ideas and Selected Topics, Volume II

• Format: ePub

Produktbeschreibung

This second volume focuses on inference in non- and semiparametric models, including topics in machine learning. It not only reexamines the procedures introduced in the authors' first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis. Numerous examples and problems illustrate statistical modeling and inference concepts. Measure theory is not required for understanding.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 485
• Erscheinungstermin: 4. November 2015
• Englisch
• ISBN-13: 9781498722711
• Artikelnr.: 59280533

Autorenporträt

Peter J. Bickel is a professor emeritus in the Department of Statistics and a professor in the Graduate School at the University of California, Berkeley. Dr. Bickel is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. He has been a Guggenheim Fellow and MacArthur Fellow, a recipient of the COPSS Presidents' Award, and president of the Bernoulli Society and the Institute of Mathematical Statistics. He holds honorary doctorate degrees from the Hebrew University of Jerusalem and ETH Zurich.

Kjell A. Doksum is a senior scientist in the Department of Statistics at the University of Wisconsin-Madison. His research encompasses the estimation of nonparametric regression and correlation curves, inference for global measures of association in semiparametric and nonparametric settings, the estimation of regression quantiles, statistical modeling and analysis of HIV data, the analysis of financial data, and Bayesian nonparametric inference.

Inhaltsangabe

Introduction and Examples. Tools for Asymptotic Analysis.
Distribution-Free, Unbiased, and Equivariant Procedures. Inference in
Semiparametric Models. Monte Carlo Methods. Nonparametric Inference for
Functions of One Variable. Prediction and Machine Learning. Appendices.
References. Indices.