مقایسهٔ روش‌ها

	رگرسیون مقاوم	رگرسیون بقا
حوزه	آمار	آمار
خانواده	Regression model	Regression model
سال پیدایش	1964	1980s
پدیدآور	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Kalbfleisch & Prentice; Cox & Oakes
نوع	Regression with outlier resistance	Parametric survival model
منبع بنیادین	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576
نام‌های دیگر	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	accelerated failure time model, AFT model, parametric survival model, time-to-event regression
مرتبط	6	3
خلاصه	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.