Negative binomial mixed models for analyzing microbiome count data

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...

Estimating Spatial and Temporal Components of Variation for Fisheries Count Data Using Negative Binomial Mixed Models

Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics ar...

Hurdle, Inflated Poisson and Inflated Negative Binomial Regression Models ‎ for Analysis of Count Data with Extra Zeros

In this paper‎, ‎we ‎propose ‎Hurdle regression models for analysing count responses with extra zeros‎. A method of estimating maximum likelihood is used to estimate model parameters. The application of the proposed model is presented in insurance dataset‎. In this example‎, there are many numbers of claims equal to zero is considered that clarify the application of the model with a zero-inflat...

Negative Binomial Loglinear Mixed Models

Functional forms for the negative binomial model for count data

This note develops an encompassing model for two well known variants of the negative binomial model (the NB1 and NB2 forms). We conclude with an application of the proposed model using the data employed in a recent health care study. © 2007 Elsevier B.V. All rights reserved.