Marginalized Zero-Altered Models for Longitudinal Count Data

Loni Philip Tabb; Eric J. Tchetgen Tchetgen; Gregory A. Wellenius; Brent A. Coull

doi:10.1007/s12561-015-9136-6

ScienceGate Book Chapters

JOURNAL ARTICLE

Marginalized Zero-Altered Models for Longitudinal Count Data

Loni Philip Tabb Eric J. Tchetgen Tchetgen Gregory A. Wellenius Brent A. Coull

Year: 2015 Journal: Statistics in Biosciences Vol: 8 (2)Pages: 181-203 Publisher: Springer Science+Business Media

DOI: 10.1007/s12561-015-9136-6

Get Full-Text PDF Get Analytical Report

Keywords:

Count data Negative binomial distribution Interpretability Poisson distribution Statistics Econometrics Mathematics Random effects model Marginal model Overdispersion Zero-inflated model Poisson regression Zero (linguistics) Covariate Variance (accounting) Regression analysis Computer science Population Artificial intelligence

Metrics

Cited By

0.86

FWCI (Field Weighted Citation Impact)

Refs

0.75

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Causal Inference Techniques

Physical Sciences → Mathematics → Statistics and Probability

Marginalized Zero-Altered Models for Longitudinal Count Data

Metrics

Citation History

Topics

Related Documents

Marginalized models for longitudinal count data

A tutorial on count regression and zero-altered count models for longitudinal substance use data.

Marginalized Zero-Inflated Bell Regression Models for Overdispersed Count Data

Supplemental Material for A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data

Modeling count data with marginalized zero-inflated distributions