Some Algorithms for the Conditional Mean Vector and Covariance Matrix

John F. Monahan

Journal of Statistical Software, 2006, vol. 016, issue i08

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.

Date: 2006-08-24
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.jstatsoft.org/index.php/jss/article/view/v016i08/v16i08.pdf
https://www.jstatsoft.org/index.php/jss/article/do ... e/v016i08/gsubex.zip

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:016:i08

DOI: 10.18637/jss.v016.i08

Access Statistics for this article

Journal of Statistical Software is currently edited by Bettina Grün, Edzer Pebesma and Achim Zeileis

More articles in Journal of Statistical Software from Foundation for Open Access Statistics
Bibliographic data for series maintained by Christopher F. Baum ().