Probability estimation via smoothing in sparse contingency tables with ordered categories

Jeffrey S. Simonoff

Statistics & Probability Letters, 1987, vol. 5, issue 1, 55-63

Abstract: Probability estimation in sparse two-dimensional contingency tables with ordered categories is examined. Several smoothing procedures are compared to analysis of the unsmoothed table. It is shown that probability estimates obtained via maximum penalized likelihood smoothing are consistent under a sparse asymptotic framework if the underlying probability matrix is smooth, and are more accurate than kernel-based and other smoothing techniques. In fact, computer simulations indicate that smoothing based on a product kernel is less effective than no smoothing at all. An example is given to illustrate the smoothing technique. Possible extensions to model building and higher dimensional tables are discussed.

Keywords: ordered; categorical; data; maximum; penalized; likelihood; kernel-based; smoothing; Bayes; methods; computer; simulations; analysis; of; association (search for similar items in EconPapers)
Date: 1987
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0167-7152(87)90027-7
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:5:y:1987:i:1:p:55-63

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 ().