Robert A. Rigby, Mikis D. Stasinopoulos, Gillian Z. Heller, Fernanda De Bastiani

Distributions for Modeling Location, Scale, and Shape (eBook, PDF)

Using GAMLSS in R

• Format: PDF

Produktbeschreibung

This is the second volume in a series of books about using the GAMLSS R package developed by the authors. This volume presents a broad overview of statistical distributions and how they can be used in practical applications.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 588
• Erscheinungstermin: 8. Oktober 2019
• Englisch
• ISBN-13: 9781000699968
• Artikelnr.: 57831424

Autorenporträt

Robert Rigby was researching in Statistics at London Metropolitan University for over 30 years specializing in distributions and advanced regression and smoothing models (for supervised learning). He is one of the two original developers of GAMLSS models. He is currently a freelance consultant.

Mikis Stasinopoulos is a statistician. He has a considerable experience in applied statistics and he is one of the two creators of GAMLSS. He worked as the director of STORM, the statistics and mathematics research centre of London Metropolitan University and now he is working as an independent statistical consultant.

Gillian Heller is Professor of Statistics at Macquarie University, Sydney. Her research interests are mainly in flexible regression models for heavy-tailed count data, with applications in biostatistics and insurance.

Fernanda De Bastiani is a permanent lecturer in the Statistics Department at Universidade Federal de Pernambuco, Brazil. Her research interests are mainly in flexible regression models, spatial data analysis and influential diagnostics in regression models.

Inhaltsangabe

Part I: Parametric distributions and the GAMLSS family of distributions. Chapter 1 Types of distributions. Chapter 2 Properties of distributions. Chapter 3 The GAMLSS Family of Distributions. Chapter 4 Continuous distributions on (
1,1). Chapter 5 Continuous distributions on (0,
). Chapter 6 Continuous distributions on (0, 1). Chapter 7 Discrete distributions for count data Chapter. 8 Binomial type distributions Chapter. 9 Mixed distributions. Part II: Advanced Topics. Chapter 10 Maximum likelihood Chapter. 11 Robustness of parameter estimation to outlier Chapter. 12 Methods of generating Chapter. 13 Discussion of skewness. Chapter 14 Discussion of Kurtosis Chapter. 15 Skewness and kurtosis comparisons of continuous distributions. Chapter 16 Heaviness of tails of continuous. Part III: Reference Guide. Chapter 17 Continuous distributions on (
,
). Chapter 18 Continuous distributions on (0,
). Chapter 19 Mixed distributions on 0 to
, including 0. Chapter 20 Continuous and mixed distributions on [0, 1]. Chapter 21 Count data. Chapter 22 Count data distributions. Chapter 23 Binomial type distributions and multinomial distributions.

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Part I: Parametric distributions and the GAMLSS family of distributions Chapter 1 Types of distributions Chapter 2 Properties of distributions Chapter 3 The GAMLSS Family of Distributions Chapter 4 Continuous distributions on (-1,1) Chapter 5 Continuous distributions on (0, ) Chapter 6 Continuous distributions on (0, 1) Chapter 7 Discrete distributions for count data Chapter 8 Binomial type distributions Chapter 9 Mixed distributions Part II: Advanced Topics Chapter 10 Maximum likelihood Chapter 11 Robustness of parameter estimation to outlier Chapter 12 Methods of generating Chapter 13 Discussion of skewness Chapter 14 Discussion of Kurtosis Chapter 15 Skewness and kurtosis comparisons of continuous distributions Chapter 16 Heaviness of tails of continuous Part III: Reference Guide Chapter 17 Continuous distributions on (- , ) Chapter 18 Continuous distributions on (0, ) Chapter 19 Mixed distributions on 0 to , including 0 Chapter 20 Continuous and mixed distributions on [0, 1] Chapter 21 Count data Chapter 22 Count data distributions 23 Binomial type distributions and multinomial distributions

Rezensionen

"...focuses on all probability distributions that can be used in GAMLSS modelling...a distributional regression framework making inroads in different fields due to its flexibility...GAMLSS's power rests on its capability to apply smoothers to numeric and categorical covariates and model numeric response variables via probability distributions other than the usual exponential family...including continuous distributions, ...discrete distributions and mixtures of continuous and discrete (mixed) distributions...This last type of distribution, although commonplace in practice, is rather ignored by applied researchers...The book has three parts. Part II ("Advanced topics") contains eight Chapters, and is perhaps the most exciting section. It deals with topics that link the GAMLSS framework and probability distributions to 'hot' topics in statistical learning."
~ Fernando Marmolejo-Ramos, Raydonal Ospina, and Freddy Hernández-Barajas, respectively University of South Australia, Universidade Federal de Pernambuco, and Universidad Nacional de Colombia sede Medellín, appeared in Australian and New Zealand Journal of Statistics, September 2022