Flexible regression and smoothing using GAMLSS in R 1st Edition by Mikis Stasinopoulos, Robert Rigby, Gillian Heller, Vlasios Voudouris, Fernanda De Bastiani 9781315269870 1315269872

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Product details:

ISBN 10: 1315269872

ISBN 13: 9781315269870

Author: Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables. Key Features: Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R. Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning. R code integrated into the text for ease of understanding and replication. Supplemented by a website with code, data and extra materials. This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.

Table of contents:

I Introduction to models and packages
1 Why GAMLSS?
2 Introduction to the gamlss packages

II Algorithms, functions and inference
3 The algorithms
4 The gamlss() function
5 Inference and prediction

III Distributions
6 The GAMLSS family of distributions
7 Finite mixture distributions

IV Model terms
8 Linear parametric additive terms
9 Additive smoothing terms
10 Random effects

V Model selection and diagnostics
11 Model selection techniques
12 Diagnostics

VI Applications
13 Centile estimation
14 Further applications

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Tags: Mikis Stasinopoulos, Robert Rigby, Gillian Heller, Flexible