The COUNTREG Procedure

Zero-Inflated Poisson Regression

ZIP Model with Logistic Link Function
ZIP Model with Standard Normal Link Function

In the zero-inflated Poisson (ZIP) regression model, the data generation process referred to earlier as Process 2 is

$g(y_{i}) = \frac{\exp (-\mu _{i})\mu _{i}^{y_{i}}}{y_{i}!}$

where $\mu _ i=e^{\mathbf{x}_{i}\bbeta }$ . Thus the ZIP model is defined as

$\displaystyle P(y_{i}=0\|\mathbf{x}_{i},\mathbf{z}_{i})$	$\displaystyle =$	$\displaystyle F_{i} + \left(1 - F_{i}\right)\exp (-\mu _{i})$
$\displaystyle P(y_{i}\|\mathbf{x}_{i},\mathbf{z}_{i})$	$\displaystyle =$	$\displaystyle \left(1- F_{i} \right)\frac{\exp (-\mu _{i}) \mu _ i^{y_{i}}}{y_{i}!},\quad y_{i}>0$

The conditional expectation and conditional variance of $y_{i}$ are given by

$E(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i}) = \mu _{i}(1 -F_{i})$

$V(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i}) = E(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i})(1+\mu _{i}F_{i})$

Note that the ZIP model (as well as the ZINB model) exhibits overdispersion since $V(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i}) >E(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i})$ .

In general, the log-likelihood function of the ZIP model is

$\mathcal{L} = \sum _{i=1}^{N}w_ i\ln \left[ P(y_{i}|\mathbf{x}_{i},\mathbf{z}_{i}) \right]$

After a specific link function (either logistic or standard normal) for the probability $\varphi _{i}$ is chosen, it is possible to write the exact expressions for the log-likelihood function and the gradient.

ZIP Model with Logistic Link Function

First, consider the ZIP model in which the probability $\varphi _{i}$ is expressed with a logistic link function—namely,

$\varphi _{i}=\frac{\exp (\mathbf{z}_{i}\bgamma )}{1+\exp (\mathbf{z}_{i}\bgamma )}$

The log-likelihood function is

$\displaystyle \mathcal{L}$	$\displaystyle =$	$\displaystyle \sum _{\{ i: y_{i}=0\} } w_ i\ln \left[\exp (\mathbf{z}_{i}’\bgamma )+\exp (-\exp (\mathbf{x}_{i}’\bbeta )) \right]$
$\displaystyle$	$\displaystyle$	$\displaystyle + \sum _{\{ i: y_{i}>0\} }w_ i\left[y_{i} \mathbf{x}_{i}’\bbeta -\exp (\mathbf{x}_{i}’\bbeta ) - \sum _{k=2}^{y_{i}}\ln (k) \right]$
$\displaystyle$	$\displaystyle$	$\displaystyle - \sum _{i=1}^{N}w_ i\ln \left[ 1 + \exp (\mathbf{z}_{i}’\bgamma ) \right]$

See Poisson Regression for the definition of .

The gradient for this model is given by

$\frac{\partial \mathcal{L}}{\partial \bgamma } = \sum _{\{ i: y_{i}=0\} } w_ i\left[\frac{\exp (\mathbf{z}_{i}\bgamma )}{\exp (\mathbf{z}_{i}\bgamma ) + \exp (-\exp (\mathbf{x}_{i}\bbeta ))}\right] \mathbf{z}_{i} - \sum _{i=1}^{N}w_ i\left[\frac{\exp (\mathbf{z}_{i}\bgamma )}{1 + \exp (\mathbf{z}_{i}\bgamma )} \right] \mathbf{z}_{i}$

$\frac{\partial \mathcal{L}}{\partial \bbeta } = \sum _{\{ i: y_{i}=0\} } w_ i\left[\frac{-\exp (\mathbf{x}_{i}\bbeta ) \exp (-\exp (\mathbf{x}_{i}\bbeta ))}{\exp (\mathbf{z}_{i}\bgamma ) + \exp (-\exp (\mathbf{x}_{i}\bbeta ))}\right] \mathbf{x}_{i} + \sum _{\{ i: y_{i}>0\} }w_ i\left[y_{i} - \exp (\mathbf{x}_{i}’\bbeta ) \right] \mathbf{x}_{i}$

ZIP Model with Standard Normal Link Function

Next, consider the ZIP model in which the probability $\varphi _{i}$ is expressed with a standard normal link function: $\varphi _{i}= \Phi (\mathbf{z}_{i}’\bgamma )$ . The log-likelihood function is

$\displaystyle \mathcal{L}$	$\displaystyle =$	$\displaystyle \sum _{\{ i: y_{i}=0\} }w_ i\ln \left\{ \Phi (\mathbf{z}_{i}’\bgamma ) + \left[ 1- \Phi (\mathbf{z}_{i}’\bgamma )\right] \exp (-\exp (\mathbf{x}_{i}’\bbeta )) \right\}$
$\displaystyle$	$\displaystyle +$	$\displaystyle \sum _{\{ i: y_{i}>0\} }w_ i\left\{ \ln \left[ \left( 1-\Phi (\mathbf{z}_{i}’\bgamma )\right) \right] - \exp (\mathbf{x}_{i}’\bbeta ) + y_{i} \mathbf{x}_{i}’\bbeta - \sum _{k=2}^{y_{i}} \ln (k) \right\}$

See Poisson Regression for the definition of .

The gradient for this model is given by

$\displaystyle \frac{\partial \mathcal{L}}{\partial \bgamma }$	$\displaystyle =$	$\displaystyle \sum _{\{ i: y_{i}=0\} } w_ i\frac{\varphi (\mathbf{z}_{i}\bgamma )\left[ 1-\exp (-\exp (\mathbf{x}_{i}\bbeta )) \right]}{\Phi (\mathbf{z}_{i}\bgamma ) + \left[ 1 - \Phi (\mathbf{z}_{i}\bgamma ) \right] \exp (-\exp (\mathbf{x}_{i}\bbeta ))} \mathbf{z}_{i}$
$\displaystyle$	$\displaystyle -$	$\displaystyle \sum _{\{ i: y_{i}>0\} } w_ i\frac{\varphi (\mathbf{z}_{i}\bgamma )}{\left[ 1 - \Phi (\mathbf{z}_{i}\bgamma ) \right]} \mathbf{z}_{i}$

$\displaystyle \frac{\partial \mathcal{L}}{\partial \bbeta }$	$\displaystyle =$	$\displaystyle \sum _{\{ i: y_{i}=0\} } w_ i\frac{-\left[1-\Phi (\mathbf{z}_{i}\bgamma )\right] \exp (\mathbf{x}_{i}\bbeta ) \exp (-\exp (\mathbf{x}_{i}\bbeta ))}{\Phi (\mathbf{z}_{i}\bgamma ) + \left[ 1 - \Phi (\mathbf{z}_{i}\bgamma ) \right] \exp (-\exp (\mathbf{x}_{i}\bbeta ))} \mathbf{x}_{i}$
$\displaystyle$	$\displaystyle +$	$\displaystyle \sum _{\{ i: y_{i}>0\} } w_ i\left[y_{i}-\exp (\mathbf{x}_{i}’\bbeta ) \right] \mathbf{x}_{i}$