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Local asymptotic mixed normality of transformed Gaussian models for random fields
Authors:Tomonari Sei
Affiliation:Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
Abstract:
Local asymptotic mixed normality (LAMN) of a class of transformed Gaussian models for discretely observed random fields is proved. The original Gaussian random field is assumed to be the product of a deterministic process and a process with independent increments. The transformed process is observed only on discrete lattice points in the unit cube and fixed domain asymptotics is investigated. This model is useful for modeling random fields with non-Gaussian marginal distributions.
Keywords:Brownian sheet   Discrete observation   Fixed domain asymptotics   Local asymptotic mixed normality   Multiparameter process   Ornstein&ndash  Uhlenbeck sheet   Random field   Transformed Gaussian model
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