Asymptotic theory of generalized information criterion for geostatistical regression model selection

Abstract

Information criteria, such as Akaike's information criterion and Bayesian information criterion are often applied in model selection. However, their asymptotic behaviors for selecting geostatistical regression models have not been well studied, particularly under the fixed domain asymptotic framework with more and more data observed in a bounded fixed region. In this article, we study the generalized information criterion (GIC) for selecting geostatistical regression models under a more general mixed domain asymptotic framework. Via uniform convergence developments of some statistics, we establish the selection consistency and the asymptotic loss efficiency of GIC under some regularity conditions, regardless of whether the covariance model is correctly or wrongly specified. We further provide specific examples with different types of explanatory variables that satisfy the conditions. For example, in some situations, GIC is selection consistent, even when some spatial covariance parameters cannot be estimated consistently. On the other hand, GIC fails to select the true polynomial order consistently under the fixed domain asymptotic framework. Moreover, the growth rate of the domain and the degree of smoothness of candidate regressors in space are shown to play key roles for model selection.

arXiv commentPublished in at http://dx.doi.org/10.1214/14-AOS1258 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Cited by in corpus (4)

• Mixed domain asymptotics for a stochastic process model with time trend and measurement error
• Asymptotic theory of generalized information criterion for geostatistical regression model selection
• Composite likelihood estimation for a gaussian process under fixed domain asymptotics
• Model Selection in High-Dimensional Misspecified Models