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A Parametric Approach to Nonparametric Statistics

  • Mayer Alvo
  • Philip L. H. Yu

Part of the Springer Series in the Data Sciences book series (SSDS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction and Fundamentals

    1. Front Matter
      Pages 1-1
    2. Mayer Alvo, Philip L. H. Yu
      Pages 3-4
    3. Mayer Alvo, Philip L. H. Yu
      Pages 5-44
    4. Mayer Alvo, Philip L. H. Yu
      Pages 45-59
  3. Nonparametric Statistical Methods

    1. Front Matter
      Pages 61-61
    2. Mayer Alvo, Philip L. H. Yu
      Pages 63-89
    3. Mayer Alvo, Philip L. H. Yu
      Pages 91-115
    4. Mayer Alvo, Philip L. H. Yu
      Pages 117-135
    5. Mayer Alvo, Philip L. H. Yu
      Pages 137-161
    6. Mayer Alvo, Philip L. H. Yu
      Pages 163-186
    7. Mayer Alvo, Philip L. H. Yu
      Pages 187-205
  4. Selected Applications

    1. Front Matter
      Pages 207-207
    2. Mayer Alvo, Philip L. H. Yu
      Pages 209-227
    3. Mayer Alvo, Philip L. H. Yu
      Pages 229-243
    4. Mayer Alvo, Philip L. H. Yu
      Pages 245-256
  5. Back Matter
    Pages 257-279

About this book

Introduction

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.

Keywords

Nonparametric statistics Statistical inference Parametric inference Ranking data Nonparametric tests Bayesian analysis Penalized likelihood

Authors and affiliations

  • Mayer Alvo
    • 1
  • Philip L. H. Yu
    • 2
  1. 1.Department of Mathematics and StatisticsUniversity of OttawaOttawaCanada
  2. 2.Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-94153-0
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-94152-3
  • Online ISBN 978-3-319-94153-0
  • Series Print ISSN 2365-5674
  • Series Online ISSN 2365-5682
  • Buy this book on publisher's site