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Διατεταγμένη Λογιστική Παλινδρόμηση (Διατεταγμένη Λογιστική/Προβίτ)×Λογιστική Παλινδρόμηση×Παλινδρόμηση Λογιστικής Πολλαπλών Ομάδων×
ΠεδίοΟικονομετρίαΕρευνητική ΣτατιστικήΟικονομετρία
ΟικογένειαRegression modelProcess / pipelineRegression model
Έτος προέλευσης198019581974
ΔημιουργόςMcCullagh (proportional odds / cumulative model)David Roxbee CoxMcFadden
ΤύποςCumulative ordinal regressionMethodMultinomial logistic regression
Θεμελιώδης πηγήMcCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗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
Εναλλακτικές ονομασίεςordinal logistic regression, proportional odds model, cumulative logit model, ordered probitlogit model, binomial logistic regression, LRmultinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Συναφείς435
ΣύνοψηOrdered 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.Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.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.
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ScholarGateΣύγκριση μεθόδων: Ordered Logit · Logistic Regression · Multinomial Logit. Ανακτήθηκε στις 2026-06-17 από https://scholargate.app/el/compare