Flexible regression and smoothing : using GAMLSS in R / Mikis D. Stasinopoulos [and four others].

Other author Stasinopoulos, Mikis D.
Format Electronic
PublicationBoca Raton : CRC Press, [2017]
Description1 online resource (xxii, 549 pages).
Supplemental ContentProQuest Ebook Central
Subjects

SeriesThe R series
Chapman & Hall/CRC the R series (CRC Press) ^A1144284
Contents Introduction to models and packages. Why GAMLSS? -- Introduction to the gamlss packages -- Algorithms, functions and inference. The algorithms -- The gamlss() function -- Inference and prediction -- Distributions. The GAMLSS family of distributions -- Finite mixture distributions -- Model terms. Linear parametric additive terms -- Additive smoothing terms -- Random effects -- Model selection and diagnostics. Model selection techniques -- Diagnostics -- Applications. Centril estimation -- Further applications.
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.
Bibliography noteIncludes bibliographical references and index.
Source of descriptionPrint version record.
Issued in other formPrint version: Flexible regression and smoothing. Boca Raton : CRC Press, [2017] 9781138197909
Genre/formElectronic books.
ISBN9781351980371 (electronic bk.)
ISBN1351980378 (electronic bk.)
ISBN9781315269870 (e-book)
ISBN1315269872 (e-book)
ISBN1351980386
ISBN9781351980388
ISBN(adobe reader)
ISBN(mobipocket)
ISBN(hardback)
ISBN(hardback)
Stock number1007457 MIL

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