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Statistical inference for functions of the covariance matrix of stationary Gaussian vector time series

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

Abstract

We consider inference for functions of the marginal covariance matrix under a class of stationary vector time series models, referred to as time-orthogonal principal components models. The main application which motivated this work involves the estimation of configurational entropy from molecular dynamics simulations in computational chemistry, where current methods of entropy estimation involve calculations based on the sample covariance matrix. The theoretical results we obtain provide a basis for approximate inference procedures, including confidence interval calculations for scalar quantities of interest; these results are applied to the molecular dynamics application, and some further applications are discussed briefly

Topics: QA276
Year: 2010
DOI identifier: 10.1007/s10463-008-0202-4
OAI identifier: oai:kar.kent.ac.uk:30342
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