Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Beta× | Regressão Logística× | Regressão Quantílica× | |
|---|---|---|---|
| Área≠ | Estatística | Estatística para pesquisa | Econometria |
| Família≠ | Regression model | Process / pipeline | Regression model |
| Ano de origem≠ | 2004 | 1958 | 1978 |
| Autor original≠ | Ferrari & Cribari-Neto | David Roxbee Cox | Koenker & Bassett |
| Tipo≠ | Generalized linear model (beta distribution) | Method | Conditional quantile regression |
| Fonte seminal≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | 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 ↗ |
| Outros nomes | beta regression model, proportion regression, Beta Regresyonu | logit model, binomial logistic regression, LR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relacionados≠ | 4 | 3 | 5 |
| Resumo≠ | Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices. | 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. |
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