Functional relationships having many independent variables and errors with multivariate normal distribution

G. R. Dolby and T. G. Freeman

Journal of Multivariate Analysis, 1975, vol. 5, issue 4, 466-479

Abstract: This paper deals with maximum likelihood estimation of linear or nonlinear functional relationships assuming that replicated observations have been made on p variables at n points. The joint distribution of the pn errors is assumed to be multivariate normal. Existing results are extended in two ways: first, from known to unknown error covariance matrix; second, from the two variate to the multivariate case. For the linear relationship it is shown that the maximum likelihood point estimates are those obtained by the method of generalized least squares. The present method, however, has the advantage of supplying estimates of the asymptotic covariances of the structural parameter estimates.

Keywords: Multivariate; functional; relationships; Errors-in-variables; model; Generalized; least; squares; Maximum; likelihood; Asymptotic; covariances (search for similar items in EconPapers)
Date: 1975
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0047-259X(75)90030-5
Full text for ScienceDirect subscribers only

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:eee:jmvana:v:5:y:1975:i:4:p:466-479

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().