A Parametric Approach to Nonparametric Statistics

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...

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Bibliographic Details
Main Author: Alvo, Mayer (Author)
Format: Electronic eBook
Language:English
Published: Cham : Springer International Publishing, 2018.
Edition:1st ed. 2018.
Series:Springer Series in the Data Sciences,
Subjects:
Probabilities.
Statistics .
ISBN:9783319941530
ISSN:2365-5674
Online Access: Get full text
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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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