Statistical inference for functions of the covariance matrix in the stationary Gaussian time-orthogonal principal components model

DOI10.1007/s10463-008-0202-4zbMath1432.62298OpenAlexW2136353445WikidataQ58419243 ScholiaQ58419243MaRDI QIDQ907026

Huiling Le, Andrew T. A. Wood, Ian L. Dryden, Alfred Kume

Publication date: 1 February 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-008-0202-4

zbMATH Keywords

shapecentral limit theoremconfigurational entropyautoregressiveprincipal componentssample covariancesize-and-shapeprocrustes

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Statistical aspects of information-theoretic topics (62B10)

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Cites Work

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• The asymptotic distribution of serial covariances
• Limit theorems for nonlinear functionals of a stationary Gaussian sequence of vectors
• Estimation and information in stationary time series
• On the Inversion of the Sample Covariance Matrix in a Stationary Autoregressive Process
• Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution (An Introduction)

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