On a fundamental optimality of the maximum likelihood estimator

Ping Cheng and James C. Fu

Statistics & Probability Letters, 1986, vol. 4, issue 4, 173-178

Abstract: It is well-known that the rate of exponential convergence for any consistent estimator is less than or equal to the Bahadur bound. In this paper we have proven, for the one-dimensional case, that the rate of exponential convergence for the maximum likelihood estimator (m.l.e.) attains the Bahadur bound if and only if the underlying distribution is a member of the exponential family of distributions.

Keywords: maximum; likelihood; estimator; exponential; rate; exponential; family (search for similar items in EconPapers)
Date: 1986
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(86)90061-1
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:stapro:v:4:y:1986:i:4:p:173-178

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

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().