Ebooks Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series)
Description Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series)
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.In 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.
Key Features:
Provides 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. Includes 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.R code integrated into the text for ease of understanding and replication.Supplemented by a website with code, data and extra materials.This 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.Read online Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series)
(PDF) Flexible regression and smoothing: Using GAMLSS in R ~ Flexible regression and smoothing: Using GAMLSS in R. March 2017; DOI: 10.1201/b21973. . Download full-text PDF Download full-text PDF Read . using a flexible generalized additive model for .
Flexible Regression and Smoothing: Using GAMLSS in R - 1st ~ 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. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the .
Flexible Regression and Smoothing: Using GAMLSS in R - 1st ~ "’Flexible Regression and Smoothing: Using GAMLSS in R’ is a comprehensive and authoritative text from the co-authors of perhaps the most flexible regression modeling framework in statistics and supervised machine learning. Traditional regression approaches focus on the mean of the distribution conditional on a set of predictor variables.
Flexible regression and smoothing : using GAMLSS in R ~ Flexible regression and smoothing : using GAMLSS in R Mikis D. Stasinopoulos , Robert A. Rigby , Gillian Z. Heller , Vlasios Voudouris , Fernanda De Bastiani This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS).
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R is a perfect way of getting started with GAMLSS, since it combines an easily accessible overview of the underlying methods with a thorough introduction to the implementation in R via the GAMLSS package family.
Package ‘gamlss’ - R ~ ri Specify ridge or lasso Regression within a GAMLSS Formula rqres.plot Creating and . Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC. (see also https://www.gamlss . Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC. 8 additive.fit
Flexible Regression and Smoothing: Using GAMLSS in R ~ Shop for Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC: The R Series) from WHSmith. Thousands of products are available to collect from store or if your order's over £20 we'll deliver for free.
gamlss function / R Documentation ~ The function gamlss() is very similar to the gam() function in S-plus (now also in R in package gam), but can fit more distributions (not only the ones belonging to the exponential family) and can model all the parameters of the distribution as functions of the explanatory variables (e.g. using linear, non-linear, smoothing, loess and random .
Flexible Regression and Smoothing: Using GAMLSS in R The R ~ Flexible Regression and Smoothing: Using GAMLSS in R (The R Series) (Englisch) Gebundene Ausgabe – 11. April 2017 von Mikis D. Stasinopoulos (Autor), Robert A. Rigby (Autor), Australia) Heller, Gillian Z. (Department of Statistics, Faculty of Science and Enginerring, Macquarie University (Autor), Vlasios Voudouris (Autor), Fernanda De Bastiani (Autor) & 2 mehr
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series) by Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani. Click here for the lowest price! Hardcover, 9781138197909, 1138197904
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series) eBook: Stasinopoulos, Mikis D., Rigby, Robert A., Heller, Gillian Z., Voudouris .
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series) - Kindle edition by Stasinopoulos, Mikis D., Rigby, Robert A., Heller, Gillian Z., Voudouris, Vlasios, De Bastiani, Fernanda. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Flexible Regression and Smoothing .
Flexible Regression and Smoothing / Taylor & Francis Group ~ Using GAMLSS in R. Flexible Regression and Smoothing . DOI link for Flexible Regression and Smoothing. Flexible Regression and Smoothing book. Using GAMLSS in R. By Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De Bastiani. Edition 1st Edition.
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 parametric distribution for the response variable and modelling all the parameters (location .
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Flexible Regression and Smoothing: Using GAMLSS in R ~ "'Flexible Regression and Smoothing: Using GAMLSS in R' is a comprehensive and authoritative text from the co-authors of perhaps the most flexible regression modeling framework in statistics and supervised machine learning. Traditional regression approaches focus on the mean of the distribution conditional on a set of predictor variables.
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R Chapman & Hall/CRC The R Series: : Stasinopoulos, Mikis D., Rigby, Robert A., Heller, Gillian Z .
Flexible Regression and Smoothing: Using GAMLSS in R ~ Flexible Regression and Smoothing: Using GAMLSS in R (Chapman & Hall/CRC The R Series) (English Edition) eBook: Stasinopoulos, Mikis D., Rigby, Robert A., Heller .
getSmo function / R Documentation ~ Arguments object. a GAMLSS fitted model. what. which distribution parameter is required, default what="mu" parameter. equivalent to what. which. which smoothing term i.e. 1, 2 etc. Note that 0 means all.
Distributions for Modeling Location, Scale, and Shape ~ GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables.