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. The name is from probability + unit.
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 1934, and a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher in an appendix to Bliss 1935.
Read more about Probit Model: Introduction, Maximum Likelihood Estimation, Berkson's Minimum Chi-square Method, Gibbs Sampling
Famous quotes containing the word model:
“Research shows clearly that parents who have modeled nurturant, reassuring responses to infants’ fears and distress by soothing words and stroking gentleness have toddlers who already can stroke a crying child’s hair. Toddlers whose special adults model kindliness will even pick up a cookie dropped from a peer’s high chair and return it to the crying peer rather than eat it themselves!”
—Alice Sterling Honig (20th century)