In modeling count data collected from manufacturing processes, economic series, disease outbreaks and ecological surveys, there are usually a relatively large or small number of zeros compared to positive counts. Such low or high frequencies of zero counts often require the use of under or over dispersed probability models for the underlying data generating mechanism. The commonly used models s...

To account for the preponderance of zero counts and simultaneous correlation of observations, a class of zero-inflated Poisson mixed regression models is applicable for accommodating the within-cluster dependence. In this paper, a score test for zero-inflation is developed for assessing correlated count data with excess zeros. The sampling distribution and the power of the test statistic are ev...

Data for discrete ordered random variables are often characterised by “excessive” zero observations. Traditional ordered probit models have limited capacity in explaining the preponderance of zero observations, especially when the zeros may relate to two distinct situations of non-participation and infrequent participation (or consumption), for example. We propose a zero-inflated ordered probit...

BACKGROUND Traditional statistical models often are based on certain presuppositions and limitations that may not presence in actual data and lead to turbulence in estimation or prediction. In these situations, artificial neural networks (ANNs) could be suitable alternative rather than classical statistical methods. STUDY DESIGN  A prospective cohort study. METHODS The study was conducted i...

The mediation analysis methodology of the cause-and-effect relationship through mediators has been increasingly popular over past decades. human microbiome can contribute to pathogenesis many complex diseases by mediating disease-leading causal pathways. However, standard is not adequate for data due excessive number zero values and over-dispersion in sequencing reads, which arise both biologic...