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Flexible regression and smoothing : using GAMLSS in R
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 pa...
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| Autres auteurs : | , , , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Flexible regression and smoothing : using GAMLSS in R / Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani |
| Publié : |
Boca Raton :
CRC Press/Taylor & Francis Group
, C. 2017 |
| Description matérielle : | 1 vol. (xxii, 549 p.) |
| Collection : | Chapman & Hall/CRC the R series (Print) |
| Contenu : | 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 |
| Sujets : |
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| 200 | 0 | |a Flexible regression and smoothing |e using GAMLSS in R |f Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani | |
| 214 | 0 | |a Boca Raton |c CRC Press/Taylor & Francis Group | |
| 214 | 4 | |d C. 2017 | |
| 215 | |a 1 vol. (xxii, 549 p.) |c ill. |d 27 cm | ||
| 225 | 0 | |a Chapman & Hall/CRC the R series | |
| 300 | |a First issued in paperback 2020 | ||
| 320 | |a Biblio. pp. 523-542. Index. | ||
| 327 | 1 | |a Introduction to models and packages. Why GAMLSS? |a Introduction to the gamlss packages |a Algorithms, functions and inference. The algorithms |a The gamlss() function |a Inference and prediction |a Distributions. The GAMLSS family of distributions |a Finite mixture distributions |a Model terms. Linear parametric additive terms |a Additive smoothing terms |a Random effects |a Model selection and diagnostics. Model selection techniques |a Diagnostics |a Applications. Centril estimation |a Further applications | |
| 330 | |a 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.-- | ||
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