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Finite mixture of skewed distributions

Cabral, Celso Romulo Barbosa.

Finite mixture of skewed distributions

Record Type:	Electronic resources : Monograph/item
Title/Author:	Finite mixture of skewed distributionsby Victor Hugo Lachos Davila, Celso Romulo Barbosa Cabral, Camila Borelli Zeller.
Author:	Davila, Victor Hugo Lachos.
other author:	Cabral, Celso Romulo Barbosa.
Published:	Cham :Springer International Publishing :2018.
Description:	x, 101 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mixture distributions (Probability theory)
Online resource:	https://doi.org/10.1007/978-3-319-98029-4
ISBN:	9783319980294$q(electronic bk.)

Finite mixture of skewed distributions
Davila, Victor Hugo Lachos.

Finite mixture of skewed distributions[electronic resource] /by Victor Hugo Lachos Davila, Celso Romulo Barbosa Cabral, Camila Borelli Zeller. - Cham :Springer International Publishing :2018. - x, 101 p. :ill., digital ;24 cm. - SpringerBriefs in statistics - ABE,2524-6917. - SpringerBriefs in statistics - ABE..

Chapter 1: Motivation -- Chapter 2: Maximum Likelihood Estimation in Normal Mixtures -- Chapter 3: Scale Mixtures of Skew-normal distributions -- Chapter 4: Univariate mixtures of SMSN distributions -- Chapter 5: Multivariate mixtures of SMSN distributions -- Chapter 6: Mixture of Regression Models.

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.

ISBN: 9783319980294$q(electronic bk.)

Standard No.: 10.1007/978-3-319-98029-4doiSubjects--Topical Terms:

224993
Mixture distributions (Probability theory)

LC Class. No.: QA273.6 / .D338 2018

Dewey Class. No.: 519.24

Finite mixture of skewed distributions
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520 $a 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.
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