R
Robert A. Rigby
Researcher at London Metropolitan University
Publications - 48
Citations - 5494
Robert A. Rigby is an academic researcher from London Metropolitan University. The author has contributed to research in topics: Kurtosis & Scale (ratio). The author has an hindex of 21, co-authored 46 publications receiving 4412 citations. Previous affiliations of Robert A. Rigby include University of North London.
Papers
More filters
Journal ArticleDOI
Generalized additive models for location, scale and shape
TL;DR: The generalized additive model for location, scale and shape (GAMLSS) as mentioned in this paper is a general class of statistical models for a univariate response variable, which assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects.
Journal ArticleDOI
Generalized Additive Models for Location Scale and Shape (GAMLSS) in R
TL;DR: GAMLSS as discussed by the authors 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.
Journal ArticleDOI
Smooth centile curves for skew and kurtotic data modelled using the Box–Cox power exponential distribution
TL;DR: The LMSP method of centile estimation is applied to modelling the body mass index of Dutch males against age by modelling each of the four parameters of the BCPE distribution as a smooth non-parametric function of an explanatory variable.
Journal ArticleDOI
Generalized Autoregressive Moving Average Models
TL;DR: A class of generalized autoregressive moving average (GARMA) models is developed that extends the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data.
Book
Flexible Regression and Smoothing: Using Gamlss in R
Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani +4 more
TL;DR: In this article, a generalized additive model for location, scale, and shape (GAMLSS) is proposed to accommodate large complex datasets, which are increasingly prevalent in the literature.