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分野統計学研究統計計量経済学
系統Regression modelProcess / pipelineRegression model
提唱年200119581978
提唱者Cantoni & Ronchetti (2001); Bondell (2008)David Roxbee CoxKoenker & Bassett
種類Robust generalized linear model (binary outcome)MethodConditional quantile regression
原典Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. 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 ↗
別名robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyonlogit model, binomial logistic regression, LRconditional quantile regression, regression quantiles, Kantil Regresyon
関連535
概要Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).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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ScholarGate手法を比較: Robust Logistic Regression · Logistic Regression · Quantile Regression. 2026-06-19に以下より取得 https://scholargate.app/ja/compare