Overview
- Includes exercises at the end of every chapter
- First book to bridge the gap between parametric and nonparametric statistics
- Contains introduction to probability and statistics
- Demonstrates Modern applications
Part of the book series: Springer Series in the Data Sciences (SSDS)
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Table of contents (12 chapters)
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Front Matter
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Introduction and Fundamentals
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Front Matter
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Nonparametric Statistical Methods
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Front Matter
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Selected Applications
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Front Matter
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Back Matter
Keywords
About this book
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.
Reviews
Authors and Affiliations
About the authors
Philip L.H. Yu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong. He received his Ph.D. from The University of Hong Kong in 1993. He is the Director of the Master of Statistics Programme. He is an Associate Editor for Computational Statistics and Data Analysis as well as for Computational Statistics. He is the author of more than 90 referred publications. His research interests include modeling of ranking data, data mining and financial and risk analytics.
Bibliographic Information
Book Title: A Parametric Approach to Nonparametric Statistics
Authors: Mayer Alvo, Philip L. H. Yu
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-319-94153-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-94152-3Published: 23 October 2018
Softcover ISBN: 978-3-030-06804-2Published: 20 December 2018
eBook ISBN: 978-3-319-94153-0Published: 12 October 2018
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XIV, 279
Number of Illustrations: 15 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods
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