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Finite Mixture of Skewed Distributions

  • Víctor Hugo Lachos Dávila
  • Celso Rômulo Barbosa Cabral
  • Camila Borelli Zeller

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the SpringerBriefs in Statistics - ABE book sub series (BRIEFSABE)

Table of contents

  1. Front Matter
    Pages i-x
  2. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 1-5
  3. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 7-13
  4. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 15-36
  5. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 37-56
  6. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 57-76
  7. Víctor Hugo Lachos Dávila, Celso Rômulo Barbosa Cabral, Camila Borelli Zeller
    Pages 77-93
  8. Back Matter
    Pages 95-101

About this book

Introduction

This book presents recent results in finite mixtures of skewed distributions to prepare readers to undertake mixture models using scale mixtures of skew normal distributions (SMSN). For this purpose, the authors consider maximum likelihood estimation for univariate and multivariate finite mixtures where components are members of the flexible class of SMSN distributions. This subclass includes the entire family of normal independent distributions, also known as scale mixtures of normal distributions (SMN), as well as the skew-normal and skewed versions of some other classical symmetric distributions: the skew-t (ST), the skew-slash (SSL) and the skew-contaminated normal (SCN), for example. These distributions have heavier tails than the typical normal one, and thus they seem to be a reasonable choice for robust inference. The proposed EM-type algorithm and methods are implemented in the R package mixsmsn, highlighting the applicability of the techniques presented in the book.

This work is a useful reference guide for researchers analyzing heterogeneous data, as well as a textbook for a graduate-level course in mixture models. The tools presented in the book make complex techniques accessible to applied researchers without the advanced mathematical background and will have broad applications in fields like medicine, biology, engineering, economic, geology and chemistry.

Keywords

regression models measurement methods bias detection agreement method validation

Authors and affiliations

  • Víctor Hugo Lachos Dávila
    • 1
  • Celso Rômulo Barbosa Cabral
    • 2
  • Camila Borelli Zeller
    • 3
  1. 1.Department of StatisticsUniversity of ConnecticutStorrs MansfieldUSA
  2. 2.Department of StatisticsFederal University of AmazonasManausBrazil
  3. 3.Department of StatisticsFederal University of Juiz de ForaJuiz de ForaBrazil

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-98029-4
  • Copyright Information The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-98028-7
  • Online ISBN 978-3-319-98029-4
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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