Asymptotic theory of generalized information criterion for geostatistical regression model selection

DOI10.1214/14-AOS1258zbMath1302.62203arXiv1412.0836OpenAlexW1999487741MaRDI QIDQ482898

Chih-Hao Chang, Hsin-Cheng Huang, Ching-Kang Ing

Publication date: 6 January 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.0836

zbMATH Keywords

Bayesian information criterionvariable selectionAkaike's information criterionselection consistencyfixed domain asymptoticincreasing domain asymptotic

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32) Statistical ranking and selection procedures (62F07) Statistical aspects of information-theoretic topics (62B10)

Related Items

Asymptotic theory of generalized information criterion for geostatistical regression model selection ⋮ Composite likelihood estimation for a Gaussian process under fixed domain asymptotics ⋮ Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model

Uses Software

• SemiPar

Cites Work

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• Fence methods for mixed model selection
• Random-Effects Models for Longitudinal Data
• Model selection in linear mixed models
• Asymptotic theory of generalized information criterion for geostatistical regression model selection
• Uniform moment bounds of Fisher's information with applications to time series
• Asymptotic properties of criteria for selection of variables in multiple regression
• Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process
• Fixed-domain asymptotic properties of tapered maximum likelihood estimators
• Spatial variation. 2nd ed
• Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
• Estimating the dimension of a model
• Interpolation of spatial data. Some theory for kriging
• Semiparametric regression model selections.
• Consistent covariate selection and post model selection inference in semiparametric regression.
• On fixed-domain asymptotics and covariance tapering in Gaussian random field models
• Order selection for same-realization predictions in autoregressive processes
• Selecting mixed-effects models based on a generalized information criterion
• Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Towards reconciling two asymptotic frameworks in spatial statistics
• Optimal Geostatistical Model Selection
• Semiparametric Regression
• Conditional Akaike information for mixed-effects models