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Introduction

  • Mayer Alvo
  • Philip L. H. Yu
Chapter
Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

This book grew out of a desire to bridge the gap between parametric and nonparametric statistics and to exploit the best aspects of the former while enjoying the robustness properties of the latter. Parametric statistics is a well-established field which incorporates the important notions of likelihood and sufficiency that are part of estimation and testing. Likelihood methods have been used to construct efficient estimators, confidence intervals, and tests with good power properties.

References

  1. Conover, W. J. and Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician, 35(3):124–129.zbMATHGoogle Scholar
  2. Neyman, J. (1937). Smooth test for goodness of fit. Skandinavisk Aktuarietidskrift, 20:149–199.zbMATHGoogle Scholar
  3. Rayner, J. C. W., Best, D. J., and Thas, O. (2009a). Generalised smooth tests of goodness of fit. Journal of Statistical Theory and Practice, pages 665–679.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

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

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