ノンパラメトリック統計学へのパラメトリック・アプローチ（テキスト） A Parametric Approach to Nonparametric Statistics (Springer Series in the Data Sciences)

Alvo, Mayer/ Yu, Philip L. H.

Springer International Publishing AG（2018/10発売）

• 製本 Hardcover:ハードカバー版／ページ数 279 p.
• 言語 ENG
• 商品コード 9783319941523

Full 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.

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.