Gaussian Markov random field spatial models in GAMLSS

Bastiani, Fernanda De, Rigby, Robert A., Stasinopoulos, Mikis D., Cysneiros, Audrey H. M. A. and Uribe-Opazo, Miguel A. (2016) Gaussian Markov random field spatial models in GAMLSS. Journal of Applied Statistics, 45 (1). pp. 168-186. ISSN 1360-0532

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Official URL: https://www.tandfonline.com/doi/abs/10.1080/026647...

Abstract / Description

This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized Additive-Model 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.

Item Type: Article
Uncontrolled Keywords: Generalized Additive Model for Location, Scale and Shape (GAMLSS); Gaussian Markov random field spatial components
Subjects: 500 Natural Sciences and Mathematics > 510 Mathematics
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 09 May 2019 14:12
Last Modified: 22 Jul 2019 08:47
URI: http://repository.londonmet.ac.uk/id/eprint/4823

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