Algorithms for the likelihood-based estimation of the random coefficient model

Chungyeol Shin and Yasuo Amemiya

Statistics & Probability Letters, 1997, vol. 32, issue 2, 189-199

Abstract: The existing algorithms for fitting the random coefficient models tend to have difficulties associated with the covariance matrix parameter space. New ML and REML algorithms are developed, explicitly addressing the parameter space problem. Theoretical justification and numerical results are presented.

Keywords: Proper; covariance; matrix; estimate; Mixed; effects; Maximum; likelihood; REML (search for similar items in EconPapers)
Date: 1997
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(96)00072-7
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:32:y:1997:i:2:p:189-199

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 ().