Strong consistency of maximum quasi-likelihood estimate in generalized linear models via a last time

Yuan-chin Ivan Chang

Statistics & Probability Letters, 1999, vol. 45, issue 3, 237-246

Abstract: In this paper, by using a last-time random variable, we show the strong consistency for the maximum quasi-likelihood estimate in generalized linear models with adaptive design variables and general link functions. Our approach is based on the Leray-Schauder Theorem and a last-time theorem. The last time that we defined here is based on a sum of martingale differences instead of independent random variables. Under some slightly stronger assumptions on the adaptive design variables, we obtain the almost sure convergence as well as the convergence rate of the estimate.

Keywords: Adaptive; design; Canonical; link; function; Generalized; linear; models; Last; time; Maximum; quasi-likelihood; estimate; Martingale; differences; Strong; consistency (search for similar items in EconPapers)
Date: 1999
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00063-2
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:45:y:1999:i:3:p:237-246

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 ().