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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Alvo, Mayer (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Yu, Philip L. H. (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Springer Series in the Data Sciences,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 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.