Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard para...

Zero-inflated data indicates that the data set contains an excessive number of zeros. The word zero-inflation is used to emphasize that the probability mass at the point zero exceeds than the one allowed under a standard parametric family of discrete distributions. Gupta et al. [1], Murat & Szynal [2], Patil & Shirke [3] have contributed to estimation and testing of the parameters involved in Z...

Zero-inflated data indicates that the data set contains an excessive number of zeros. The word zero-inflation is used to emphasize that the probability mass at the point zero exceeds than the one allowed under a standard parametric family of discrete distributions. Gupta et al. [1], Murat & Szynal [2], Patil & Shirke [3] have contributed to estimation and testing of the parameters involved in Z...

We introduce the generalized zero-inflated allocation scheme of placing n labeled balls into N labeled cells. We study the asymptotic behavior of the number of empty cells when (n,N) belongs to the “right” and “left” domain of attraction. An application to the estimation of characteristics of agreement among a set of raters which independently classify subjects into one of two categories is als...

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with t...

In this study, two-state Markov switching count data models are proposed as an alternative to zero-inflated models, in order to account for preponderance of zeros typically observed in accident frequency data. Similar to zero-inflated models, two-state Markov switching models assume an existence of two states of roadway safety. One of the states is a zero-accident state, which is safe. The othe...

Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated, containing more zeros than would be expected from the standard error distributions used in GLMMs, e.g., parasite counts may be exactly zero fo...

SUMMARY In psychosocial and behavioral studies count outcomes recording the frequencies of the occurrence of some health or behavior outcomes (such as the number of unprotected sexual behaviors during a period of time) often contain a preponderance of zeroes because of the presence of 'structural zeroes' that occur when some subjects are not at risk for the behavior of interest. Unlike random z...

A recommendation system confronts two opposing problems. In order to be practical, recommendations systems need to be quick, efficient, and scale well in both computational complexity and memory cost [7, 23]. This is frequently achieved by reducing the dimensionality of the data [7]. For example, suppose we have N users, M items, and a review matrix R ∈ RN×M consisting of positive integer ratin...

Excess zeros and overdispersion are commonly encountered phenomena that limit the use of traditional Poisson regression models for modeling count data. The focus of this paper is on modeling count data in the case that a population has excess zero counts and also consists of several sub-populations in the non-zero counts. The proposed zero-inflated Poisson regression mixture model accounts for ...