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.

A structural-zero component in a count mixture produces a count identically equal to zero. If its mixture weight is $\pi$ and the ordinary component also assigns mass $p_0$ to zero, the observed zero probability is $\pi +(1-\pi )p_0$. Thus an observed zero does not reveal the latent component membership. A shared component for a repeated count profile makes the whole profile zero and induces dependence between its components.

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.

Let $B=0$ with probability $\pi$, and otherwise let $B$ have a Gamma distribution with mean one and variance $\tau$. Given $B$, counts $Y_j$ are conditionally independent Poisson random variables with means $B{\mu }_j$. For $q=1-\pi$, the law of total variance and law of total covariance giveEach component has a zero-inflated negative binomial distribution, but the components are not independent after marginalizing $B$. The zero component is shared by the whole profile. At $\tau =0$, interpret the positive Gamma component as a point mass at one.

If $\mathbb{E}(Y_j\mid B,x)=B\exp ({\beta }_0+{\beta }^{T}x+{\alpha }_j)$ and the distribution of $B$ is the same at each observation time with finite positive mean, integrating out $B$ adds $\log \mathbb{E}B$ to the marginal log-mean intercept. The conditional positive-$B$ mean ratio and the marginal mean ratio both equal $e^{{\alpha }_j-{\alpha }_k}$. This is a property of the multiplicative logarithmic link function; it need not hold for nonlinear links such as the logit link.

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.