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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. Probit model - Wikipedia

    en.wikipedia.org/wiki/Probit_model

    A probit model is a popular specification for a binary response model. As such it treats the same set of problems as does logistic regression using similar techniques. When viewed in the generalized linear model framework, the probit model employs a probit link function. [2]

  4. Ordinal regression - Wikipedia

    en.wikipedia.org/wiki/Ordinal_regression

    Another approach is given by Rennie and Srebro, who, realizing that "even just evaluating the likelihood of a predictor is not straight-forward" in the ordered logit and ordered probit models, propose fitting ordinal regression models by adapting common loss functions from classification (such as the hinge loss and log loss) to the ordinal case ...

  5. Discrete choice - Wikipedia

    en.wikipedia.org/wiki/Discrete_choice

    Logit models can be estimated by logistic regression, and probit models can be estimated by probit regression. Nonparametric methods, such as the maximum score estimator , have been proposed. [ 28 ] [ 29 ] Estimation of such models is usually done via parametric, semi-parametric and non-parametric maximum likelihood methods, [ 30 ] but can also ...

  6. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    The multivariate probit model is a standard method of estimating a joint relationship between several binary dependent variables and some independent variables. For categorical variables with more than two values there is the multinomial logit. For ordinal variables with more than two values, there are the ordered logit and ordered probit models.

  7. 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 .

  8. Probit - Wikipedia

    en.wikipedia.org/wiki/Probit

    In probability theory and statistics, the probit function is the quantile function associated with the standard normal distribution. It has applications in data analysis and machine learning, in particular exploratory statistical graphics and specialized regression modeling of binary response variables .

  9. Multivariate probit model - Wikipedia

    en.wikipedia.org/wiki/Multivariate_probit_model

    In statistics and econometrics, the multivariate probit model is a generalization of the probit model used to estimate several correlated binary outcomes jointly. For example, if it is believed that the decisions of sending at least one child to public school and that of voting in favor of a school budget are correlated (both decisions are binary), then the multivariate probit model would be ...