Negative binomial regression

Abstract

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Female hurricanes are deadlier than male hurricanes

“…Variance of deaths (1673.152) is much greater than the mean of deaths (20.652), indicating a high likelihood of overdispersion and spurious estimates of SEs and P values. In such cases, a negative binomial regression model is recommended (26,27).…”

“…Nine independent coders who were blind to the hypothesis rated the masculinity vs. femininity of historical hurricane names on two items (1 = very masculine, 11 = very feminine, and 1 = very man-like, 11 = very woman-like), which were averaged to compute a masculinity-femininity index (MFI). A series of negative binomial regression analyses (26,27) were performed to investigate effects of perceived masculinity-femininity of hurricane names (MFI), minimum pressure, normalized damage (NDAM) (28), and the interactions among them on the number of deaths caused by the hurricanes (see Materials and Methods for complete descriptions of models tested, Table S1 for descriptive statistics, and Table S2 for a statistical summary of models tested. See the full Dataset S1 available online.…”

The habitual female offender inside: How psychopathic traits predict chronic prison violence.

“…The negative binomial dispersion parameters for nonviolent misconducts (α = 5.32, p < .001) and violent misconducts (α = 7.34, p < .001) were significantly greater than zero, suggesting the data were overdispersed. Negative binomial regression corrects for overdispersion, therefore producing more reliable estimates (Cameron & Trivedi 1998;Hilbe, 2011). We conducted a zero inflated negative binomial regression to compare the model fit with the negative binomial model.…”

Socioeconomic Determinants of Exposure to Alcohol Outlets

“…Independent variables were the local and lagged market potential within SA1 units, local and lagged SA1 income, dry and retail zoning, and SA2 market potential and income. The spatial units are not of uniform size, so all models also controlled for land area (logged km 2 ) (Hilbe, 2011). We specified uninformed priors for all random effects and allowed a Markov Chain Monte Carlo burn-in of 300,000 iterations before sampling 50,000 iterations to provide a 95% credible interval (which can be interpreted similarly to a confidence interval from a regular regression model).…”