Normal view MARC view ISBD view

Flexible regression and smoothing: using GAMLSS in R

By: Stasinopoulos, Milis D.
Contributor(s): Rigby, Robert A [Co-author] | Heller, Gillian Z [Co-author] | Voudouris, Vlasios [Co-author] | Bastiani, Fernanda De [Co-author].
Material type: materialTypeLabelBookSeries: Chapman and Hall - CRC: the R series. Publisher: Boca Raton CRC Press Taylor and Francis Group 2017Description: xxii, 549 p. 27 cm.ISBN: 9781138197909.Subject(s): Regression analysis - data processing | Linear models - Statistics | Smoothing - statistics | Big data | R - computer program language | Computer science - statistical analysisDDC classification: 519.536028553 Summary: 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. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials. https://www.crcpress.com/Flexible-Regression-and-Smoothing-Using-GAMLSS-in-R/Stasinopoulos-Rigby-Heller-Voudouris-Bastiani/p/book/9781138197909
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
Slot 1428 (0 Floor, East Wing) Non-fiction 519.53602 8553 S8F5 (Browse shelf) Available 195167

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. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.

https://www.crcpress.com/Flexible-Regression-and-Smoothing-Using-GAMLSS-in-R/Stasinopoulos-Rigby-Heller-Voudouris-Bastiani/p/book/9781138197909

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha