Local asymptotic mixed normality of transformed Gaussian models for random fields

Tomonari Sei

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)375-398
Number of pages24
JournalStochastic Processes and their Applications
Volume117
Issue number3
DOIs
Publication statusPublished - 2007 Mar

Fingerprint

Gaussian Model
Normality
Random Field
Processes with Independent Increments
Unit cube
Gaussian Random Field
Lattice Points
Marginal Distribution
Modeling
Random field
Model
Class

Keywords

  • Brownian sheet
  • Discrete observation
  • Fixed domain asymptotics
  • Local asymptotic mixed normality
  • Multiparameter process
  • Ornstein-Uhlenbeck sheet
  • Random field
  • Transformed Gaussian model

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Mathematics(all)
  • Statistics and Probability
  • Modelling and Simulation

Cite this

Local asymptotic mixed normality of transformed Gaussian models for random fields. / Sei, Tomonari.

In: Stochastic Processes and their Applications, Vol. 117, No. 3, 03.2007, p. 375-398.

Research output: Contribution to journalArticle

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