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Regresi Logistik Bertertib (Logit/Probit Bertertib)×Regresi Logistik×Regresi Logistik Multinomial×
BidangEkonometrikStatistik PenyelidikanEkonometrik
KeluargaRegression modelProcess / pipelineRegression model
Tahun asal198019581974
PengasasMcCullagh (proportional odds / cumulative model)David Roxbee CoxMcFadden
JenisCumulative ordinal regressionMethodMultinomial logistic regression
Sumber perintisMcCullagh, 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
Aliasordinal 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
Berkaitan435
RingkasanOrdered 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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ScholarGateBandingkan kaedah: Ordered Logit · Logistic Regression · Multinomial Logit. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare