{"product_id":"flexible-regression-and-smoothing-using-gamlss-in-r-paperback","title":"Flexible Regression and Smoothing: Using Gamlss in R - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMikis D. Stasinopoulos\u003c\/b\u003e (Author), \u003cb\u003eRobert A. Rigby\u003c\/b\u003e (Author), \u003cb\u003eGillian Z. Heller\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eIn 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. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eKey Features: \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cli\u003eProvides 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. \u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cli\u003eIncludes 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.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cli\u003eR code integrated into the text for ease of understanding and replication.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e \u003cli\u003eSupplemented by a website with code, data and extra materials.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eMikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 572\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.16 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 30, 2020\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52705017364787,"sku":"9780367658069","price":122.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/RFRjRU5xR0RnMDJaMkZFb2k4dnRsUT09.webp?v=1763369971","url":"https:\/\/www.vysn.com\/products\/flexible-regression-and-smoothing-using-gamlss-in-r-paperback","provider":"VYSN","version":"1.0","type":"link"}