GAMLSS is a general framework for fitting regression type models where the distribution of the response variable does not have to belong to the exponential family and includes highly skew and kurtotic continuous and discrete distribution. GAMLSS allows all the parameters of the distribution of the response variable to be modelled as linear/non-linear or smooth functions of the explanatory variables. This paper starts by defining the statistical framework of GAMLSS, then describes the current implementation of GAMLSS in R and finally gives four different data examples to demonstrate how GAMLSS can be used for statistical modelling
A solution to the problem of having to deal with a large number of interrelated explanatory variable...
In generalized additive models for location, scale and shape (GAMLSS), the response distribution is ...
This paper introduces distributional regression also known as generalized additive models for locati...
GAMLSS is a general framework for fitting regression type models where the distribu-tion of the resp...
A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here u...
This book is about learning from data using the Generalized Additive Models for Location, Scale and ...
Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric mod...
Generalized additive models for location, scale and shape (GAMLSS) are a flexible class of regressio...
The validity of estimation and smoothing parameter selection for the wide class of generalized addit...
Abstract: The Box–Cox t (BCT) distribution is presented as a model for a dependent variable Y exhibi...
This paper describes the modelling and fitting of Gaussian Markov random field spatial components wi...
The validity of estimation and smoothing parameter selection for the wide class of generalized addit...
The generalized additive models for location, scale and shape (GAMLSS) developed by Rigby and Stasin...
In general, real life’s effects are not linear. To identify and interpret better the phenomena of ...
In statistics, linear modelling techniques are widely used methods to explain one variable by others...
A solution to the problem of having to deal with a large number of interrelated explanatory variable...
In generalized additive models for location, scale and shape (GAMLSS), the response distribution is ...
This paper introduces distributional regression also known as generalized additive models for locati...
GAMLSS is a general framework for fitting regression type models where the distribu-tion of the resp...
A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here u...
This book is about learning from data using the Generalized Additive Models for Location, Scale and ...
Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric mod...
Generalized additive models for location, scale and shape (GAMLSS) are a flexible class of regressio...
The validity of estimation and smoothing parameter selection for the wide class of generalized addit...
Abstract: The Box–Cox t (BCT) distribution is presented as a model for a dependent variable Y exhibi...
This paper describes the modelling and fitting of Gaussian Markov random field spatial components wi...
The validity of estimation and smoothing parameter selection for the wide class of generalized addit...
The generalized additive models for location, scale and shape (GAMLSS) developed by Rigby and Stasin...
In general, real life’s effects are not linear. To identify and interpret better the phenomena of ...
In statistics, linear modelling techniques are widely used methods to explain one variable by others...
A solution to the problem of having to deal with a large number of interrelated explanatory variable...
In generalized additive models for location, scale and shape (GAMLSS), the response distribution is ...
This paper introduces distributional regression also known as generalized additive models for locati...