Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/103215
Title: Estimating structured correlation matrices in smooth Gaussian random field models
Authors: Loh, W.-L. 
Lam, T.-K. 
Keywords: Computer experiment
Sieve maximum likelihood estimation
Smooth Gaussian random field
Strong consistency
Structured correlation matrix
Issue Date: 2000
Source: Loh, W.-L.,Lam, T.-K. (2000). Estimating structured correlation matrices in smooth Gaussian random field models. Annals of Statistics 28 (3) : 880-904. ScholarBank@NUS Repository.
Abstract: This article considers the estimation of structured correlation matrices in infinitely differentiable Gaussian random field models. The problem is essentially motivated by the stochastic modeling of smooth deterministic responses in computer experiments. In particular, the log-likelihood function is determined explicitly in closed-form and the sieve maximum likelihood estimators are shown to be strongly consistent under mild conditions.
Source Title: Annals of Statistics
URI: http://scholarbank.nus.edu.sg/handle/10635/103215
ISSN: 00905364
Appears in Collections:Staff Publications

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