Gaussian Markov random field spatial models in GAMLSS

被引：15

作者：

De Bastiani, Fernanda ^{[1
,2
]}

Rigby, Robert A. ^{[3
]}

Stasinopoulous, Dimitrios M. ^{[3
]}

Cysneiros, Audrey H. M. A. ^{[1
]}

Uribe-Opazo, Miguel A. ^{[4
]}

机构：

[1] Univ Fed Pernambuco, Dept Stat, Recife, PE, Brazil

[2] Pontificia Univ Catolica Chile, Dept Stat, Santiago, Chile

[3] London Metropolitan Univ, STORM, London, England

[4] Univ Estadual Oeste Parana, Postgrad Program Agr Engn, Cascavel, PR, Brazil

来源：

JOURNAL OF APPLIED STATISTICS | 2018年 / 45卷 / 01期

关键词：

Discrete spatial analysis; flexible regression; intrinsic autoregressive model; random effects; rent data; SELECTION;

D O I：

10.1080/02664763.2016.1269728

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：168 / 186

页数：19

共 50 条

[1] A comparative study of Gaussian geostatistical models and Gaussian Markov random field models
Song, Hae-Ryoung
Fuentes, Montserrat
Ghosh, Sujit
[J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2008, 99 (08) : 1681 - 1697
[2] Scaling intrinsic Gaussian Markov random field priors in spatial modelling
Sorbye, Sigrunn Holbek
Rue, Havard
[J]. SPATIAL STATISTICS, 2014, 8 : 39 - 51
[3] Utilizing Gaussian Markov Random Field Properties of Bayesian Animal Models
Steinsland, Ingelin
Jensen, Henrik
[J]. BIOMETRICS, 2010, 66 (03) : 763 - 771
[4] Approximate Bayesian inference for hierarchical Gaussian Markov random field models
Rue, Havard
Martino, Sara
[J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2007, 137 (10) : 3177 - 3192
[5] A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields
Nosedal-Sanchez, Alvaro
Jackson, Charles S.
Huerta, Gabriel
[J]. GEOSCIENTIFIC MODEL DEVELOPMENT, 2016, 9 (07) : 2407 - 2414
[6] Nonstationary Spatial Gaussian Markov Random Fields
Yue, Yu
Speckman, Paul L.
[J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2010, 19 (01) : 96 - 116
[7] IMAGE SYNTHESIS AND CODING USING GAUSSIAN-MARKOV RANDOM FIELD MODELS
CHELLAPPA, R
BAGDAZIAN, R
[J]. PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 1982, 359 : 328 - 335
[8] TEXTURE SYNTHESIS AND COMPRESSION USING GAUSSIAN-MARKOV RANDOM FIELD MODELS
CHELLAPPA, R
CHATTERJEE, S
BAGDAZIAN, R
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1985, 15 (02): : 298 - 303
[9] Snake based Unsupervised Texture Segmentation using Gaussian Markov Random Field Models
Mahmoodi, Sasan
Gunn, Steve
[J]. 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011,
[10] CLASSIFICATION OF ROTATED AND SCALED TEXTURED IMAGES USING GAUSSIAN MARKOV RANDOM FIELD MODELS
COHEN, FS
FAN, ZG
PATEL, MA
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1991, 13 (02) : 192 - 202

← 1 2 3 4 5 →