Probit Function Family

From GM-RKB
Jump to navigation Jump to search

A Probit Function Family is a function family that is restricted to probit functions (of the form of the form [math]\displaystyle{ P_i = \frac{1}{\sqrt[\sigma]{2\pi}} \intop^{U_i}_{-\infty} \exp\{-1/2(\frac{x-\mu}{\sigma})^2\}\rd x }[/math])



References

2014

  • (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/probit_model Retrieved:2014-6-3.
    • 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. [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, if estimated probabilities greater than 1/2 are treated as classifying an observation into a predicted category, the probit model 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; a fast method for computing maximum likelihood estimates for them was proposed by Ronald Fisher as an appendix to Bliss' work in 1935.
  1. Oxford English Dictionary, 3rd ed. s.v. probit (article dated June 2007):
  2. Ordinal probit regression model UCLA Academic Technology Services http://www.ats.ucla.edu/stat/stata/dae/ologit.htm