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Regresi Logistik×Model Plackett-Luce×
BidangStatistik PenyelidikanPembuatan Keputusan
KeluargaProcess / pipelineRegression model
Tahun asal19581975
PengasasDavid Roxbee CoxRobin Plackett; R. Duncan Luce
JenisMethodProbabilistic ranking model
Sumber perintisCox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C, 24(2), 193–202. DOI ↗
Aliaslogit model, binomial logistic regression, LRLuce's Choice Axiom Model, Rank-Ordered Logit Model, Exploded Logit Model, Sıralama Tercih Modeli
Berkaitan33
RingkasanLogistic 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.The Plackett-Luce model is a probabilistic framework for analysing and predicting rank-ordered data. Introduced by Robin Plackett (1975) — building on R. Duncan Luce's earlier axiom of choice (1959) — it models the probability of any complete ranking of items as a sequential selection process, where each item's chance of being chosen at each position is proportional to its latent worth parameter. It is widely used in preference learning, recommender systems, and choice modelling.
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ScholarGateBandingkan kaedah: Logistic Regression · Plackett-Luce Model. Dicapai 2026-06-19 daripada https://scholargate.app/ms/compare