Zero inflation

Zero inflation augments a count distribution with an additional mass at zero. An observed zero can arise either from the structural-zero component or from the ordinary count component; the latent source is not observed.

With structural-zero probability $\pi$, size $r>0$ and count mean $\lambda$, the model assigns $\pi +(1-\pi )(r/(r+\lambda ){)}^r$ to zero and $(1-\pi )f_{\mathrm{NB}}(y;r,\lambda )$ to positive counts. Its expectation is $(1-\pi )\lambda$ and its variance is $(1-\pi )(\lambda +{\lambda }^2/r)+\pi (1-\pi ){\lambda }^2$, by the law of total variance. A logarithmic link function can relate the count-component mean to predictors.

For known size $r$ and ${\lambda }_i=e^{x_i^T\beta }$, the expectation-maximization algorithm gives structural-zero responsibility $t_i=0$ for positive counts and $t_i=\pi /[\pi +(1-\pi )(r/(r+{\lambda }_i){)}^r]$ for zero counts. This is Bayes theorem. Maximizing the expected complete-data log-likelihood gives ${\pi }_{\mathrm{new}}=n^{-1}{\sum }_i{t}_i$ and a weighted negative binomial regression with weights $1-t_i$. The objective separates into a Bernoulli mixing term and a weighted count term, which proves the update.