JSM 2004 - Toronto

Abstract #300034

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Activity Number: 288
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract - #300034
Title: RKHS Algorithms for Estimation in Random Fields
Author(s): Christopher G. Small*+
Companies: University of Waterloo
Address: Department of Statistics & Actuarial Sc., Waterloo, ON, N2L 3G1, Canada
Keywords: random field ; correlated data ; estimating function
Abstract:

In both geostatistical and biomedical applications, data arise which require the estimation of moments when the data are highly correlated. For random fields, which are sampled at finitely many prespecified landmarks, the usual approach is to estimate the mean function of the process using a weighted least-squares estimate whose form is determined in part by inverting the covariance matrix of the sampled process. However, when the covariance matrix is a function of the parameters and the sampling design saturates the domain of observation, this solution becomes computationally problematic. I will propose an RKHS algorithm with linear convergence to the required parameter estimates.

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