Model selection with misspecified spatial covariance structure

Xu, Lin, Wang, You-Gan, Zheng, Shurong and Shi, Ning-Zhong (2014) Model selection with misspecified spatial covariance structure. Journal of Statistical Computation and Simulation, 85 11: 2276-2294. doi:10.1080/00949655.2014.926551


Author Xu, Lin
Wang, You-Gan
Zheng, Shurong
Shi, Ning-Zhong
Title Model selection with misspecified spatial covariance structure
Journal name Journal of Statistical Computation and Simulation   Check publisher's open access policy
ISSN 0094-9655
1563-5163
Publication date 2014-06-16
Year available 2014
Sub-type Article (original research)
DOI 10.1080/00949655.2014.926551
Open Access Status
Volume 85
Issue 11
Start page 2276
End page 2294
Total pages 19
Place of publication Abingdon, Oxfordshire, United Kingdom
Publisher Taylor & Francis
Collection year 2015
Language eng
Subject 1804 Statistics, Probability and Uncertainty
2611 Modelling and Simulation
2613 Statistics and Probability
2604 Applied Mathematics
Abstract Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square (Formula presented.), and overall RIC performs well.
Keyword Spatial data analysis
Information criterion
Correlation function
Variance function
Mean function
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 16 June 2014.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
 
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