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Statistical Estimation for Truncated Exponential Families

  • Masafumi¬†Akahira

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

About this book

Introduction

This book presents new findings on nonregular statistical estimation. Unlike other books on this topic, its major emphasis is on helping readers understand the meaning and implications of both regularity and irregularity through a certain family of distributions. In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. This focus includes the (truncated) Pareto distribution, which is widely used in various fields such as finance, physics, hydrology, geology, astronomy, and other disciplines. The family is essential in that it links both regular and nonregular distributions, as it becomes a regular exponential family if the truncation parameter is known. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter. In order to obtain more information on the truncation, the Bayesian approach is also considered. Further, the application to some useful truncated distributions is discussed. The illustrated clarification of the nonregular structure provides researchers and practitioners with a solid basis for further research and applications.

Keywords

Truncated Exponential Family Natural Parameter Truncation Parameter Maximum Likelihood Estimator Second Order Asymptotic Loss

Authors and affiliations

  • Masafumi¬†Akahira
    • 1
  1. 1.Institute of MathematicsUniversity of TsukubaTsukubaJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-5296-5
  • Copyright Information The Author(s) 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-981-10-5295-8
  • Online ISBN 978-981-10-5296-5
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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