Probit model

In statistics, a probit model is a type of regression where the dependent variable can only take two values, for example married or not married.

A probit model is a popular specification for an ordinal or a binary response model that employs a probit link function. This model is most often estimated using standard maximum likelihood procedure, such an estimation being called a probit regression.

Probit models were introduced by Chester Bliss in 1935, and a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher in an appendix to the same article.

Introduction

Suppose response variable Y is binary, that is it can have only two possible outcomes which we will denote as 1 and 0. For example Y may represent presence/absence of a certain condition, success/failure of some device, answer yes/no on a survey, etc. We also have a vector of regressors X, which are assumed to influence the outcome Y. Specifically, we assume that the model takes form

$\Pr(Y=1 \mid X) = \Phi(X'\beta),$

where Pr denotes probability, and Φ is the Cumulative Distribution Function (CDF) of the standard normal distribution. The parameters β are typically estimated by maximum likelihood.

It is also possible to motivate the probit model as a latent variable model. Suppose there exists an auxiliary random variable

$Y^\ast = X'\beta + \varepsilon, \,$

where ε ~ N(0, 1). Then Y can be viewed as an indicator for whether this latent variable is positive:

$Y = \mathbf{1}_{\{Y^\ast>0\}} = \begin{cases} 1 & \text{if }Y^\ast > 0 \ \text{ i.e. } - \varepsilon < X'\beta, \\ 0 &\text{otherwise.} \end{cases}$

Maximum likelihood estimation

Suppose data set $\{y_i,x_i\}_{i=1}^n$ contains n independent statistical units corresponding to the model above. Then their joint log-likelihood function is

$\ln\mathcal{L}(\beta) = \sum_{i=1}^n \bigg( y_i\ln\Phi(x_i'\beta) + (1-y_i)\ln\!\big(1-\Phi(x_i'\beta)\big) \bigg)$

The estimator $\hat\beta$ which maximizes this function will be consistent, asymptotically normal and efficient provided that E[XX'] exists and is not singular. It can be shown that this log-likelihood function is globally concave in β, and therefore standard numerical algorithms for optimization will converge rapidly to the unique maximum.

Asymptotic distribution for $\hat\beta$ is given by

$\sqrt{n}(\hat\beta - \beta)\ \xrightarrow{d}\ \mathcal{N}(0,\,\Omega^{-1}),$

where

$\Omega = \operatorname{E}\bigg[ \frac{\varphi^2(X'\beta)}{\Phi(X'\beta)(1-\Phi(X'\beta))}XX' \bigg], \qquad \hat\Omega = \frac{1}{n}\sum_{i=1}^n \frac{\varphi^2(x'_i\hat\beta)}{\Phi(x'_i\hat\beta)(1-\Phi(x'_i\hat\beta))}x_ix'_i$

and φ = Φ' is the Probability Density Function (PDF) of standard normal distribution.

Berkson's minimum chi-square method

This method can be applied only when there are many observations of response variable $y_i$ having the same value of the vector of regressors $x_i$ (such situation may be referred to as “many observations per cell”). More specifically, the model can be formulated as follows.

Suppose among n observations $\{y_i,x_i\}_{i=1}^n$ there are only T distinct values of the regressors, which can be denoted as $\{x_{(1)},\ldots,x_{(T)}\}$. Let $n_t$ be the number of observations with $x_i=x_{(t)}$, and $r_t$ the number of observations with $x_i=x_{(t)}$ and $y_i=1$. We assume that there are indeed “many” observations per each “cell”: limit n_t÷n → const_t>0 as n→∞ and for each group t.

Denote

$\hat{p}_t = r_t/n_t$

$\hat\sigma_t^2 = \frac{1}{n_t} \frac{\hat{p}_t(1-\hat{p}_t)}{\varphi^2\big(\Phi^{-1}(\hat{p}_t)\big)}$

Then Berkson's minimum chi-square estimator is a generalized least squares estimator in a regression of $\Phi^{-1}(\hat{p}_t)$ on $x_{(t)}$ with weights $\hat\sigma_t^{-2}$:

$\hat\beta = \Bigg( \sum_{t=1}^T \hat\sigma_t^{-2}x_{(t)}x'_{(t)} \Bigg)^{-1} \sum_{t=1}^T \hat\sigma_t^{-2}x_{(t)}\Phi^{-1}(\hat{p}_t)$

It can be shown that this estimator is consistent (as n→∞ and T fixed), asymptotically normal and efficient.^{[citation needed]} Its advantage is the presence of a closed-form formula for the estimator. However, it is only meaningful to carry out this analysis when individual observations are not available, only their aggregated counts $r_t$, $n_t$, and $x_{(t)}$ (for example in the analysis of voting behavior).

See also

• Generalized linear model
• Logit model
• Limited dependent variable
• Multivariate probit models
• Ordered probit and Ordered logit model
• Separation (statistics)

References

• Bliss, C.I. (1935). "The calculation of the dosage-mortality curve". Annals of Applied Biology (22)134–167. doi:10.1111/j.1744-7348.1935.tb07713.x
• Bliss, C.I (1938). "The determination of the dosage-mortality curve from small numbers". Quarterly Journal of Pharmacology (11)192–216.
• McCullagh, Peter; John Nelder (1989). Generalized Linear Models. London: Chapman and Hall. ISBN 0-412-31760-5. 
• Albert, J.H., and Chib, S. (1993). "Bayesian Analysis of Binary and Polychotomous Response Data." Journal of the American Statistical Association

(88)422: pp. 669-679. http://www.jstor.org/stable/2290350