A Lindley–binomial model for analyzing the proportions with sparseness and excessive zeros

Abstract

Proportional data arise frequently in a wide variety of fields of study. Such data often exhibit extra variation such as over/under dispersion, sparseness and zero inflation. For example, the hepatitis data present both sparseness and zero inflation with 19 contributing non-zero denominators of 5 or less and with 36 having zero seropositive out of 83 annual age groups. The whitefly data consists of 640 observations with 339 zeros (53%), which demonstrates extra zero inflation. The catheter management data involve excessive zeros with over 60% zeros averagely for outcomes of 193 urinary tract infections, 194 outcomes of catheter blockages and 193 outcomes of catheter displacements. However, the existing models cannot always address such features appropriately. In this paper, a new two-parameter probability distribution called Lindley–binomial (LB) distribution is proposed to analyze the proportional data with such features. The probabilistic properties of the distribution such as moment, moment generating function are derived. The Fisher scoring algorithm and EM algorithm are presented for the computation of estimates of parameters in the proposed LB regression model. The issues on goodness of fit for the LB model are discussed. A limited simulation study is also performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates. The proposed model is illustrated through three aforementioned proportional datasets.

Keywords:

• Proportional data
• EM algorithm
• Lindley distribution
• binomial distribution
• overdispersion
• sparseness
• zero inflation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors are very grateful to editor, associate editor and two referees for their careful reading and valuable comments which have greatly improved this paper. The research of first author is partially supported by Natural Sciences and Engineering Research Council of Canada(NSERC).