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minb
Multiple-Inflated Negative Binomial Model
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Maintained by
Mingcong Wu
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First Published: 2023-10-01
Latest Update: 2023-10-01
Description:
Count data is prevalent and informative, with widespread
application in many fields such as social psychology, personality, and
public health. Classical statistical methods for the analysis of count
outcomes are commonly variants of the log-linear model, including
Poisson regression and Negative Binomial regression. However, a
typical problem with count data modeling is inflation, in the sense
that the counts are evidently accumulated on some integers. Such an
inflation problem could distort the distribution of the observed
counts, further bias estimation and increase error, making the classic
methods infeasible. Traditional inflated value selection methods based
on histogram inspection are easy to neglect true points and
computationally expensive in addition. Therefore, we propose a
multiple-inflated negative binomial model to handle count data
modeling with multiple inflated values, achieving data-driven inflated
value selection. The proposed approach provides simultaneous
identification of important regression predictors on the target count
response as well. More details about the proposed method are described in
Li, Y., Wu, M., Wu, M., & Ma, S. (2023) .
How to cite:
Mingcong Wu (2023). minb: Multiple-Inflated Negative Binomial Model. R package version 0.1.0, https://cran.r-project.org/web/packages/minb. Accessed 06 Mar. 2026.
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