Gaussian Markov random field spatial models in GAMLSS

Fernanda De Bastiani, Robert A. Rigby, Dimitrios M. Stasinopoulous, Audrey H.M.A. Cysneiros and Miguel A. Uribe-Opazo

Journal of Applied Statistics, 2018, vol. 45, issue 1, 168-186

Abstract: This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized AdditiveModel for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data.

Date: 2018
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DOI: 10.1080/02664763.2016.1269728

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