Distributions for Modeling Location, Scale, and Shape

Distributions for Modeling Location, Scale, and Shape
Author :
Publisher : CRC Press
Total Pages : 544
Release :
ISBN-10 : 9781000701180
ISBN-13 : 1000701182
Rating : 4/5 (182 Downloads)

Book Synopsis Distributions for Modeling Location, Scale, and Shape by : Robert A. Rigby

Download or read book Distributions for Modeling Location, Scale, and Shape written by Robert A. Rigby and published by CRC Press. This book was released on 2019-10-08 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. Key features: Describes over 100 distributions, (implemented in the GAMLSS packages in R), including continuous, discrete and mixed distributions. Comprehensive summary tables of the properties of the distributions. Discusses properties of distributions, including skewness, kurtosis, robustness and an important classification of tail heaviness. Includes mixed distributions which are continuous distributions with additional specific values with point probabilities. Includes many real data examples, with R code integrated in the text for ease of understanding and replication. Supplemented by the gamlss website. This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables, and to those interested in the properties of distributions.

Distributions for Modeling Location, Scale, and Shape Related Books

Distributions for Modeling Location, Scale, and Shape
Language: en
Pages: 544
Authors: Robert A. Rigby
Categories: Mathematics
Type: BOOK - Published: 2019-10-08 - Publisher: CRC Press

GET EBOOK

This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the dis
Flexible Regression and Smoothing
Language: en
Pages: 641
Authors: Mikis D. Stasinopoulos
Categories: Mathematics
Type: BOOK - Published: 2017-04-21 - Publisher: CRC Press

GET EBOOK

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models
Generalized Additive Models for Location, Scale, and Shape
Language: en
Pages: 0
Authors: Mikis D. Stasinopoulos
Categories: Regression analysis
Type: BOOK - Published: 2024 - Publisher:

GET EBOOK

"This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum lik
Generalized Additive Models
Language: en
Pages: 412
Authors: Simon Wood
Categories: Mathematics
Type: BOOK - Published: 2006-02-27 - Publisher: CRC Press

GET EBOOK

Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tib
Generalized Additive Models for Location, Scale and Shape
Language: en
Pages: 307
Authors: Mikis D. Stasinopoulos
Categories: Mathematics
Type: BOOK - Published: 2024-02-29 - Publisher: Cambridge University Press

GET EBOOK

A comprehensive presentation of generalized additive models for location, scale and shape linking methods with diverse applications.