Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
[test by reto]
Authors: John F. Monahan
Title: Some Algorithms for the Conditional Mean Vector and Covariance Matrix
Abstract: We consider here the problem of computing the mean vector and covariance matrix for a conditional normal distribution, considering especially a sequence of problems where the conditioning variables are changing. The sweep operator provides one simple general approach that is easy to implement and update. A second, more goal-oriented general method avoids explicit computation of the vector and matrix, while enabling easy evaluation of the conditional density for likelihood computation or easy generation from the conditional distribution. The covariance structure that arises from the special case of an ARMA(p, q) time series can be exploited for substantial improvements in computational efficiency.

Page views:: 6283. Submitted: 2005-10-01. Published: 2006-08-24.
Paper: Some Algorithms for the Conditional Mean Vector and Covariance Matrix     Download PDF (Downloads: 6382)
Supplements: gsubex .f95 examples Download (Downloads: 1113; 15KB)

DOI: 10.18637/jss.v016.i08

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.