Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Statistical Modelling
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (2)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Eilers, P. H.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Articles

Ill-posed problems with counts, the composite link model and penalized likelihood

Paul HC Eilers

Paul HC Eilers, Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, PO Box 80140, 3508 TC Utrecht, The Netherlands. E-mail: P.H.C.Eilers{at}uu.nl

Certain data sets with distributions or counts can be interpreted as indirect observations of latent distributions or (time) series of counts. The structure of such data matches elegantly with the composite link model (CLM). The parameters can be estimated with iteratively re-weighted linear regression. Unfortunately, the estimating equations generally are singular or severely ill-conditioned. An effective solution is to impose smoothness on the solution, by penalizing the likelihood with a roughness measure. The optimal smoothing parameter is found efficiently by minimizing Akaike's Information Criterion (AIC). Several applications are presented.

Key Words: back-calculation • mixtures • negative binomial distribution • over-dispersion

Statistical Modelling, Vol. 7, No. 3, 239-254 (2007)
DOI: 10.1177/1471082X0700700302
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Statistical ModellingHome page
C. G. Camarda, P. H.C. Eilers, and J. Gampe
Modelling general patterns of digit preference
Statistical Modeling, December 1, 2008; 8(4): 385 - 401.
[Abstract] [PDF]