Порівняння методів

	Біваріативна пробіт-модель	Мультиноміальна логістична регресія	Упорядкована логістична регресія (Ordered Logit/Probit)
Галузь	Економетрика	Економетрика	Економетрика
Родина	Regression model	Regression model	Regression model
Рік появи	1970	1974	1980
Автор методу	J. R. Ashford & R. R. Sowden	McFadden	McCullagh (proportional odds / cumulative model)
Тип	Maximum-likelihood binary outcome model	Multinomial logistic regression	Cumulative ordinal regression
Основоположне джерело	Ashford, J. R., & Sowden, R. R. (1970). Multi-variate probit analysis. Biometrics, 26(3), 535–546.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142.
Інші назви	Bivariate Binary Probit, Joint Probit Model, Two-Equation Probit, İki Değişkenli Probit	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit
Пов'язані	3	5	4
Підсумок	The Bivariate Probit Model, introduced by Ashford and Sowden (1970), jointly estimates two binary outcome equations whose error terms are allowed to be correlated. By modeling both outcomes simultaneously under a bivariate normal distribution, it corrects for the dependence between decisions that separate probit regressions would ignore, producing consistent and efficient parameter estimates for researchers studying interrelated binary choices.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes.