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Activity Number: 379
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #311378 View Presentation
Title: Some Information Criteria for Selecting Geostatistical Regression Models
Author(s): Hsin-Cheng Huang*+ and Chih-Hao Chang and Ching-Kang Ing
Companies: Academia Sinica and Academia Sinica and Academia Sinica
Keywords: Akaike's information criterion ; Bayesian information criterion ; fixed domain asymptotic ; increasing domain asymptotic ; selection consistency ; variable 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 talk, the asymptotic theory of information criteria will be given under both the fixed and increasing domain asymptotic frameworks. The growth rate of the domain and smoothness of candidate regressors in space are shown to play key roles for model selection.


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