Flexible regression and smoothing

using GAMLSS in R

D. Mikis Stasinopoulos, Robert Rigby, Gillian Heller, Vlasios Voudouris, Fernanda De Bastiani

    Research output: Book/ReportBook

    57 Citations (Scopus)

    Abstract

    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.
    Original languageEnglish
    Place of PublicationBoca Raton, Florida
    PublisherChapman & Hall/CRC
    Number of pages549
    ISBN (Electronic)9781351980388
    ISBN (Print)9781138197909
    DOIs
    Publication statusPublished - 2017

    Publication series

    NameChapman and Hall/CRC the R Series

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    Cite this

    Stasinopoulos, D. M., Rigby, R., Heller, G., Voudouris, V., & De Bastiani, F. (2017). Flexible regression and smoothing: using GAMLSS in R. (Chapman and Hall/CRC the R Series). Boca Raton, Florida: Chapman & Hall/CRC. https://doi.org/10.1201/b21973