Iterative proportional scaling via decomposable submodels for contingency tables

Details Iterative proportional scaling via decomposable submodels for contingency tables Iterative proportional scaling via decomposable submodels for contingency tables

2006-03-21

arxiv.org/abs/math/0603495v2

Computational Statistics and Data Analysis, Vol. 53, 966-978. (2009)

Mathematics

Statistics Theory

Scientific paper

10.1016/j.csda.2008.11.013

We propose iterative proportional scaling (IPS) via decomposable submodels for maximizing likelihood function of a hierarchical model for contingency tables. In ordinary IPS the proportional scaling is performed by cycling through the elements of the generating class of a hierarchical model. We propose to adjust more marginals at each step. This is accomplished by expressing the generating class as a union of decomposable submodels and cycling through the decomposable models. We prove convergence of our proposed procedure, if the amount of scaling is adjusted properly at each step. We also analyze the proposed algorithms around the maximum likelihood estimate (MLE) in detail. Faster convergence of our proposed procedure is illustrated by numerical examples.

Affiliated with

Also associated with

No associations

Rating

Iterative proportional scaling via decomposable submodels for contingency tables does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Iterative proportional scaling via decomposable submodels for contingency tables, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Iterative proportional scaling via decomposable submodels for contingency tables will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-302471