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  2. Ordered logit - Wikipedia

    en.wikipedia.org/wiki/Ordered_logit

    In statistics, the ordered logit model or proportional odds logistic regression is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. [1]

  3. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/.../Multinomial_logistic_regression

    Multinomial logistic regression is known by a variety of other names, including polytomous LR, [2] [3] multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model. [4]

  4. Ordinal regression - Wikipedia

    en.wikipedia.org/wiki/Ordinal_regression

    [1] [2] Examples of ordinal regression are ordered logit and ordered probit. Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference (on a scale from, say, 1–5 for "very poor" through "excellent"), as well as in information retrieval.

  5. Discrete choice - Wikipedia

    en.wikipedia.org/wiki/Discrete_choice

    Discrete choice models take many forms, including: Binary Logit, Binary Probit, Multinomial Logit, Conditional Logit, Multinomial Probit, Nested Logit, Generalized Extreme Value Models, Mixed Logit, and Exploded Logit. All of these models have the features described below in common.

  6. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    This illustrates how the logit serves as a link function between the probability and the linear regression expression. Given that the logit ranges between negative and positive infinity, it provides an adequate criterion upon which to conduct linear regression and the logit is easily converted back into the odds. [2]

  7. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    For ordinal variables with more than two values, there are the ordered logit and ordered probit models. Censored regression models may be used when the dependent variable is only sometimes observed, and Heckman correction type models may be used when the sample is not randomly selected from the population of interest.

  8. Category:Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Category:Logistic_regression

    Pages in category "Logistic regression" ... Logit analysis in marketing; M. Multinomial logistic regression; O. Ordered logit; S. Separation (statistics)

  9. Multinomial probit - Wikipedia

    en.wikipedia.org/wiki/Multinomial_probit

    In statistics and econometrics, the multinomial probit model is a generalization of the probit model used when there are several possible categories that the dependent variable can fall into. As such, it is an alternative to the multinomial logit model as one method of multiclass classification .