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

Kume, Alfred and Dryden, Ian L and Le, Huiling and Wood, Andrew T.A. (2010) Statistical inference for functions of the covariance matrix of stationary Gaussian vector time series. Annals of the Institute of Statistical Mathematics, 62 (5). pp. 967-994. ISSN 0020-3157. (doi:https://doi.org/10.1007/s10463-008-0202-4) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User:	Alfred Kume
Date Deposited:	11 Oct 2012 06:47 UTC
Last Modified:	04 Jul 2014 15:54 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/30342 (The current URI for this page, for reference purposes)