Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión Logística× | Regresión Cuantílica× | |
|---|---|---|
| Campo≠ | Estadística para la investigación | Econometría |
| Familia≠ | Process / pipeline | Regression model |
| Año de origen≠ | 1958 | 1978 |
| Autor original≠ | David Roxbee Cox | Koenker & Bassett |
| Tipo≠ | Method | Conditional quantile regression |
| Fuente seminal≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias | logit model, binomial logistic regression, LR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateConjunto de datos ↗ |
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