Sentence examples for model for counts from inspiring English sources

Within a ZIP likelihood framework, Long et al. (2014) proposed marginalized zero-inflated Poisson (MZIP) regression, which specifies a two-part model for counts with a set of regression coefficients for the marginal mean and, to complete model specification, a second set of regression coefficients for the latent parameter defining membership in the 'excess-zero' class.

This has been modelled via the negative binomial regression model which generally is the most appropriate model for counts with overdispersed values.4 From a substantive viewpoint the negative binomial distribution reflects the theoretical explanations of repeat victimisation which, as seen earlier, is extensive in both years (Hilbe 2011; Tseloni 1995; Tseloni and Pease 2010).

A natural model for counts is the single-parameter Poisson distribution.

In this manuscript, we advocated a model that specifies free per-gene dispersion parameters in the Negative Binomial model for counts of RNA-seq reads.

It has been established that a Poisson model for counts with stratum indicators gives identical estimates and can allow for these phenomena [ 2], but it is little used, probably because of the overheads in estimating many stratum parameters.

It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters.

The Poisson distribution is a popular model for count data.

We consider another bivariate time series model for count data to decide the relationship of the bivariate claim counts among the different periods.

It is therefore expected that the proposed MLFD with its interesting features and flexibility will be a useful addition as a model for count data.

with μ ij being the mean of the j th component distribution, is a popular finite mixture model for count data.

Based on this excavation inventory a specific regression model for count data was developed that permitted an area-wide prediction of the larva densities taking into account significant causal covariate effects and the spatial autocorrelation of the data.