Sentence examples for model for count from inspiring English sources

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.

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

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.

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.

The negative binomial (NB) distribution has broad applications as a model for count data, particularly for data exhibiting overdispersion (i.e. with sample variance exceeding the mean).

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).

For example, to reproduce the functionality of the previous CDKAromaticity class: Here, the CDK model for counting donated electrons is used, along with the rings systems that were identified by the older algorithm in previous versions that was limited in the number of fused rings systems that were considered.

Somewhere in the perplexity of data gathering, the model for counting poor Americans changed from just looking at food and wage standards, to adding government benefits and household expenses.

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