Semicontinuous data consist of a combination of a point-mass at zero and a positive skewed distribution. This type of non-negative data distribution is found in data from many fields, but presents unique challenges for analysis. Specifically, these data cannot be analyzed using positive distributions, but distributions that are unbounded are also likely a poor fit. Two-part models incorporate b...

background: the number of failed courses and semesters in students are indicatorsof their performance. these amounts have zero inflated (zi) distributions. using zi poisson and negative binomial distributions we can model these count data to find the associated factors and estimate the parameters. this study aims at to investigate the important factors related to the educational performance of ...

Although statistical models fit many citation data sets reasonably well with the best fitting models being the hooked power law and discretised lognormal distribution, the fits are rarely close. One possible reason is that there might be more uncited articles than would be predicted by any model if some articles are inherently uncitable. Using data from 23 different Scopus categories, this arti...

Applications in which data take nonnegative values but have a substantial proportion of values at zero occur in many disciplines. The modeling of such “clumped-at-zero” or “zero-inflated” data is challenging. We survey models that have been proposed. We consider cases in which the response for the non-zero observations is continuous and in which it is discrete. For the continuous and then the d...

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...

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...

Real-life count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. The result of...

AIM The aim of this study was to compare alternatives methods for analysis of zero inflated count data and compare them with simple count models that are used by researchers frequently for such zero inflated data. BACKGROUND Analysis of viral load and risk factors could predict likelihood of achieving sustain virological response (SVR). This information is useful to protect a person from acqu...