A Parametric Approach to Nonparametric Statistics

• Main Authors: Alvo, Mayer (Author, http://id.loc.gov/vocabulary/relators/aut), Yu, Philip L. H. (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Springer Series in the Data Sciences,
• Subjects: Probabilities.; Statistics .; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.

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...

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.