Probit: Difference between revisions
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:<math>\Phi^{-1}(p)=\sqrt{2}\,\operatorname{erf}^{-1}(2p-1)</math> |
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The probit model was developed by [[C. |
The probit model was developed by [[C.I. Bliss]] in 1934. A popoular, but almost identical, alternative to probit is the [[logit]] model. |
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Revision as of 08:57, 20 November 2006
In probability theory and statistics the probit function is the inverse cumulative distribution function, or quantile function of the normal distribution.
The probit function is often denoted as and is of type:
Like the logit (log odds) function, it may be used to transform a variable ranging over the interval into a derived quantity ranging over the real numbers. This has applications in probit models, which are generalized linear models.
The probit function may be expressed in terms of the inverse of the error function (this can almost a definition of the error function):
The probit model was developed by C.I. Bliss in 1934. A popoular, but almost identical, alternative to probit is the logit model.
Bliss, C. I. (1934). The method of probits. Science 79:38-39.