Probit model
Part of a series on Statistics 
Regression analysis 

Models 
Estimation 
Background 
In statistics, a probit model is a type of regression where the dependent variable can take only two values, for example married or not married. The word is a portmanteau, coming from probability + unit.^{[1]} The purpose of the model is to estimate the probability that an observation with particular characteristics will fall into a specific one of the categories; moreover, classifying observations based on their predicted probabilities is a type of binary classification model.
A probit model is a popular specification for an ordinal^{[2]} or a binary response model. As such it treats the same set of problems as does logistic regression using similar techniques. The probit model, which employs a probit link function, is most often estimated using the standard maximum likelihood procedure, such an estimation being called a probit regression.
Probit models were introduced by Chester Bliss in 1934;^{[3]} a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher as an appendix to Bliss' work in 1935.^{[4]}
Contents
Conceptual framework
Suppose a 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 the form
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 possible to motivate the probit model as a latent variable model. Suppose there exists an auxiliary random variable
where ε ~ N(0, 1). Then Y can be viewed as an indicator for whether this latent variable is positive:
The use of the standard normal distribution causes no loss of generality compared with using an arbitrary mean and standard deviation because adding a fixed amount to the mean can be compensated by subtracting the same amount from the intercept, and multiplying the standard deviation by a fixed amount can be compensated by multiplying the weights by the same amount.
To see that the two models are equivalent, note that
Model estimation
Maximum likelihood estimation
Suppose data set contains n independent statistical units corresponding to the model above. Then their joint loglikelihood function is
The estimator 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 loglikelihood function is globally concave in β, and therefore standard numerical algorithms for optimization will converge rapidly to the unique maximum.
Asymptotic distribution for is given by
where
and is the Probability Density Function (PDF) of standard normal distribution.
Berkson's minimum chisquare method
This method can be applied only when there are many observations of response variable having the same value of the vector of regressors (such situation may be referred to as "many observations per cell"). More specifically, the model can be formulated as follows.
Suppose among n observations there are only T distinct values of the regressors, which can be denoted as . Let be the number of observations with and the number of such observations with . We assume that there are indeed "many" observations per each "cell": for each .
Denote
Then Berkson's minimum chisquare estimator is a generalized least squares estimator in a regression of on with weights :
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 closedform formula for the estimator. However, it is only meaningful to carry out this analysis when individual observations are not available, only their aggregated counts , , and (for example in the analysis of voting behavior).
Gibbs sampling
Gibbs sampling of a probit model is possible because regression models typically use normal prior distributions over the weights, and this distribution is conjugate with the normal distribution of the errors (and hence of the latent variables Y^{*}). The model can be described as
From this, we can determine the full conditional densities needed:
The result for β is given in the article on Bayesian linear regression, although specified with different notation.
The only trickiness is in the last two equations. The notation is the Iverson bracket, sometimes written or similar. It indicates that the distribution must be truncated within the given range, and rescaled appropriately. In this particular case, a truncated normal distribution arises. Sampling from this distribution depends on how much is truncated. If a large fraction of the original mass remains, sampling can be easily done with rejection sampling—simply sample a number from the nontruncated distribution, and reject it if it falls outside the restriction imposed by the truncation. If sampling from only a small fraction of the original mass, however (e.g. if sampling from one of the tails of the normal distribution—for example if is around 3 or more, and a negative sample is desired), then this will be inefficient and it becomes necessary to fall back on other sampling algorithms. General sampling from the truncated normal can be achieved using approximations to the normal CDF and the probit function, and R has a function rtnorm()
for generating truncatednormal samples.
Model evaluation
The suitability of an estimated binary model can be evaluated by counting the number of true observations equaling 1, and the number equaling zero, for which the model assigns a correct predicted classification by treating any estimated probability above 1/2 (or, below 1/2), as an assignment of a prediction of 1 (or, of 0). See Logistic regression § Model suitability for details.
See also
 Generalized linear model
 Limited dependent variable
 Logit model
 Multinomial probit
 Multivariate probit models
 Ordered probit and Ordered logit model
 Separation (statistics)
 Tobit model
References

^ Oxford English Dictionary, 3rd ed. s.v. probit (article dated June 2007): Bliss, C. I. (1934). "The Method of Probits". Science. 79 (2037): 38–39. doi:10.1126/science.79.2037.38. PMID 17813446.
These arbitrary probability units have been called ‘probits’.
 ^ Ordinal probit regression model UCLA Academic Technology Services http://www.ats.ucla.edu/stat/stata/dae/ologit.htm
 ^ Bliss, C. I. (1934). "The Method of Probits". Science. 79 (2037): 38–39. doi:10.1126/science.79.2037.38. PMID 17813446.
 ^ Fisher, R. A. (1935). "The Case of Zero Survivors in Probit Assays". Annals of Applied Biology. 22: 164–165. doi:10.1111/j.17447348.1935.tb07713.x. Archived from the original on 20140430.
Further reading
 Albert, J. H.; Chib, S. (1993). "Bayesian Analysis of Binary and Polychotomous Response Data". Journal of the American Statistical Association. 88 (422): 669–679. doi:10.1080/01621459.1993.10476321. JSTOR 2290350.
 Amemiya, Takeshi (1985). "Qualitative Response Models". Advanced Econometrics. Oxford: Basil Blackwell. pp. 267–359. ISBN 0631133453.
 Bliss, C. I. (1935). "The calculation of the dosagemortality curve". Annals of Applied Biology. 22: 134–167. doi:10.1111/j.17447348.1935.tb07713.x.
 Bliss, C. I. (1938). "The determination of the dosagemortality curve from small numbers". Quarterly Journal of Pharmacology. 11: 192–216.
 Gouriéroux, Christian (2000). "The Simple Dichotomy". Econometrics of Qualitative Dependent Variables. New York: Cambridge University Press. pp. 6–37. ISBN 0521589851.
 McCullagh, Peter; John Nelder (1989). Generalized Linear Models. London: Chapman and Hall. ISBN 0412317605.
External links
 Econometrics Lecture (topic: Probit model) on YouTube by Mark Thoma