📄 y_e_count.hlp
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{smcl}
{p 0 4}
{help contents:Top}
> {help y_stat:Statistics}
> {help y_est:Estimation}
> {help y_est0:Regression models}
{bind:> {bf:Count data}}
{p_end}
{hline}
{title:Help file listings}
{p 4 8 4}
{bf:{help poisson:Poisson count-data regression}}{break}
Poisson maximum-likelihood regression & goodness-of-fit test
{p 4 8 4}
{bf:{help nbreg:Negative binomial regression}}{break}
negative binomial (Poisson with overdispersion)
maximum-likelihood regression
{p 4 8 4}
{bf:{help nbreg:Generalized negative binomial regression}}{break}
the overdispersion parameter can be modeled
{p 4 8 4}
{bf:{help zip:Zero-inflated Poisson regression}}{break}
maximum-likelihood Poisson estimation when the number of zeros is inflated
{p 4 8 4}
{bf:{help ztp:Zero-truncated Poisson regression}}{break}
maximum-likelihood Poisson estimation when the zeros are trucated
{p 4 8 4}
{bf:{help zinb:Zero-inflated negative binomial regression}}{break}
maximum-likelihood negative binomial estimation when the number
of zeros is inflated
{p 4 8 4}
{bf:{help ztnb:Zero-truncated negative binomial regression}}{break}
maximum-likelihood negative binomial regression where the zeros
are truncated
{p 4 8 4}
{bf:{help glm:Generalized linear model}}{break}
Poisson and negative binomial families with many choices for link
INCLUDE help ypostnote
{hline}
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