Name: Flexible Regression and Smoothing: Using Gamlss in r (Chapman & Hall (en Inglés)
Price: 204625.00 COP
Availability: InStock
Author: Mikis D. Stasinopoulos; Robert A. Rigby; Gillian Z. Heller; Vlasios Voudouris; Fernanda De Bastiani
ISBN: 9780367658069

portada Flexible Regression and Smoothing: Using Gamlss in r (Chapman & Hall (en Inglés)

Formato

Libro Físico

Autor

Mikis D. Stasinopoulos; Robert A. Rigby; Gillian Z. Heller; Vlasios Voudouris; Fernanda De Bastiani

Año

2020

Idioma

Inglés

N° páginas

571

Encuadernación

Tapa Blanda

ISBN13

9780367658069

N° edición

Categorías

Probabilidad y estadística

Flexible Regression and Smoothing: Using Gamlss in r (Chapman & Hall (en Inglés)

Mikis D. Stasinopoulos; Robert A. Rigby; Gillian Z. Heller; Vlasios Voudouris; Fernanda De Bastiani (Autor) · · Tapa Blanda

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Reseña del libro "Flexible Regression and Smoothing: Using Gamlss in r (Chapman & Hall (en Inglés)"

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.