Zero-inflated negative binomial model
= Zero-inflated negative binomial model
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 $(1-\pi)f_{\rm NB}(y;r,\lambda)$ 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.