A Parametric Approach to Nonparametric Statistics
Titre:
A Parametric Approach to Nonparametric Statistics
ISBN (Numéro international normalisé des livres):
9783319941530
Auteur personnel:
Edition:
1st ed. 2018.
PRODUCTION_INFO:
Cham : Springer International Publishing : Imprint: Springer, 2018.
Description physique:
XIV, 279 p. 15 illus. in color. online resource.
Collections:
Springer Series in the Data Sciences,
Table des matières:
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.
Extrait:
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.
Auteur ajouté:
Auteur collectif ajouté:
Langue:
Anglais