An asymptotic expansion for the distribution of asymptotic maximum likelihood estimators of vector parameters

R. Michel

Journal of Multivariate Analysis, 1975, vol. 5, issue 1, 67-82

Abstract: It is shown that the probability that a suitably standardized asymptotic maximum likelihood estimator of a vector parameter (i.e., an estimator which approximates the solution of the likelihood equation in a reasonably good way) lies in a measurable convex set can be approximated by an integral involving a multidimensional normal density function and a series in n-1/2 with certain polynomials as coefficients.

Keywords: Asymptotic; maximum; likelihood; estimators; of; a; vector; parameter; asymptotic; expansion; of; asymptotic; maximum; likelihood; estimators; Edgeworth; expansion; convex; sets (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)90056-1
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:1:p:67-82

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 ().