The TCOUNTREG Procedure (Experimental)

Zero-Inflated Count Regression Overview

The main motivation for zero-inflated count models is that real-life data frequently display overdispersion and excess zeros. Zero-inflated count models provide a way of modeling the excess zeros in addition to allowing for overdispersion. In particular, for each observation, there are two possible data generation processes. The result of a Bernoulli trial is used to determine which of the two processes is used. For observation $i$, Process 1 is chosen with probability $\varphi _{i}$ and Process 2 with probability $1-\varphi _{i}$. Process 1 generates only zero counts. Process 2 generates counts from either a Poisson or a negative binomial model. In general,

\[  y_ i \sim \left\{  \begin{array}{l@{\quad \mbox {with probability} \quad }l} 0 &  \varphi _{i} \\ g(y_ i) &  1-\varphi _{i} \end{array} \right.  \]

Therefore, the probability of $\{  Y_{i} = y_{i} \} $ can be described as

$\displaystyle  P(y_{i}=0|\mathbf{x}_{i})  $
$\displaystyle  =  $
$\displaystyle  \varphi _{i} + (1-\varphi _{i})g(0)  $
$\displaystyle P(y_{i}|\mathbf{x}_{i})  $
$\displaystyle  =  $
$\displaystyle  (1-\varphi _{i})g(y_{i}), \quad y_{i}>0  $

where $g(y_ i)$ follows either the Poisson or the negative binomial distribution. You can specify the probability $\varphi $ with the PROBZERO= option in the OUTPUT statement.

When the probability $\varphi _{i}$ depends on the characteristics of observation $i$, $\varphi _{i}$ is written as a function of $\mathbf{z}_{i}’\bgamma $, where $\mathbf{z}_ i’$ is the $1 \times (q+1)$ vector of zero-inflation covariates and $\bgamma $ is the $(q+1) \times 1$ vector of zero-inflation coefficients to be estimated. (The zero-inflation intercept is $\gamma _0$; the coefficients for the $q$ zero-inflation covariates are $\gamma _1, \ldots , \gamma _ q$.) The function $F$ that relates the product $\mathbf{z}_{i}’\bgamma $ (which is a scalar) to the probability $\varphi _{i}$ is called the zero-inflation link function,

\[  \varphi _{i} = F_{i} = F(\mathbf{z}_{i}’\bgamma )  \]

In the TCOUNTREG procedure, the zero-inflation covariates are indicated in the ZEROMODEL statement. Furthermore, the zero-inflation link function $F$ can be specified as either the logistic function,

\[  F(\mathbf{z}_{i}’\bgamma ) = \Lambda (\mathbf{z}_{i}’\bgamma ) = \frac{\exp (\mathbf{z}_{i}\bgamma )}{1+\exp (\mathbf{z}_{i}\bgamma )}  \]

or the standard normal cumulative distribution function (also called the probit function),

\[  F(\mathbf{z}_{i}’\bgamma ) = \Phi (\mathbf{z}_{i}’\bgamma ) = \int _{0}^{\mathbf{z}_{i}\bgamma } \frac{1}{\sqrt {2 \pi }}\exp (-u^2 \slash 2) du  \]

The zero-inflation link function is indicated in the LINK option in ZEROMODEL statement. The default ZI link function is the logistic function.