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.
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.
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.
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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