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| Логистична регресия× | Модел на Plackett-Luce× | |
|---|---|---|
| Област≠ | Статистика за изследвания | Вземане на решения |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 1958 | 1975 |
| Създател≠ | David Roxbee Cox | Robin Plackett; R. Duncan Luce |
| Тип≠ | Method | Probabilistic ranking model |
| Основополагащ източник≠ | Cox, 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 ↗ |
| Други названия≠ | logit model, binomial logistic regression, LR | Luce's Choice Axiom Model, Rank-Ordered Logit Model, Exploded Logit Model, Sıralama Tercih Modeli |
| Свързани | 3 | 3 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
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