A Parametric Approach to Nonparametric Statistics
Series: Springer Series in the Data Sciences;
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22 690 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Edition number Softcover reprint of the original 1st ed. 2018
- Publisher Springer
- Date of Publication 20 December 2018
- Number of Volumes 1 pieces, Previously published in hardcover
- ISBN 9783030068042
- Binding Paperback
- No. of pages279 pages
- Size 279x210 mm
- Weight 759 g
- Language English
- Illustrations 15 Illustrations, color 0
Categories
Short description:
This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.
This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.
MoreLong description:
This book demonstrates that nonparametric statistics can be taught from a parametric point of view. As a result, one can exploit various parametric tools such as the use of the likelihood function, penalized likelihood and score functions to not only derive well-known tests but to also go beyond and make use of Bayesian methods to analyze ranking data. The book bridges the gap between parametric and nonparametric statistics and presents the best practices of the former while enjoying the robustness properties of the latter.
This book can be used in a graduate course in nonparametrics, with parts being accessible to senior undergraduates. In addition, the book will be of wide interest to statisticians and researchers in applied fields.
?The book is interesting and well written. Theoretical results and formulas derived are illustrated by various numerical examples. The majority of chapters are equipped with interesting exercises for the readers.? (Jonas Šiaulys, zbMath 1416.62006, 2019) More
Table of Contents:
I. Introduction and Fundamentals.- Introduction.- Fundamental Concepts in Parametric Inference.- II. Modern Nonparametric Statistical Methods.- Smooth Goodness of Fit Tests.- One-Sample and Two-Sample Problems.- Multi-Sample Problems.- Tests for Trend and Association.- Optimal Rank Tests.- Efficiency.- III. Selected Applications.- Multiple Change-Point Problems.- Bayesian Models for Ranking Data.- Analysis of Censored Data.- A. Description of Data Sets.
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A Parametric Approach to Nonparametric Statistics
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