Multiplicative models for frequency data, estimation and testing
Statistics Theory
2018-04-17 v4 Statistics Theory
Abstract
This paper is about models for a vector of probabilities whose elements must have a multiplicative structure and sum to 1 at the same time; in certain applications, as basket analysis, these models may be seen as a constrained version of quasi-independence. After reviewing the basic properties of these models, their geometric features as a curved exponential family are investigated. A new algorithm for computing maximum likelihood estimates is presented and new insights are provided on the underlying geometry. The asymptotic distribution of three statistics for hypothesis testing are derived and a small simulation study is presented to investigate the accuracy of asymptotic approximations.
Cite
@article{arxiv.1704.06762,
title = {Multiplicative models for frequency data, estimation and testing},
author = {Antonio Forcina},
journal= {arXiv preprint arXiv:1704.06762},
year = {2018}
}