เปรียบเทียบวิธี

	Robust Quantile-on-Quantile Regression	การถดถอยแบบทนทาน
สาขาวิชา	เศรษฐมิติ	สถิติศาสตร์
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015–2020s	1964
ผู้ริเริ่ม	Sim and Zhou (2015) for QQ regression; robust extensions developed subsequently in the literature	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
ประเภท	Nonparametric quantile regression	Regression with outlier resistance
แหล่งต้นตำรับ	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
ชื่อเรียกอื่น	RQQR, robust QQ regression, robust quantile-on-quantile, outlier-robust QQR	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
ที่เกี่ยวข้อง	3	6
สรุป	Robust Quantile-on-Quantile Regression extends the QQ framework of Sim and Zhou (2015) by adding resistance to outliers and heavy-tailed distributions. It estimates how each quantile of one variable responds to each quantile of another, producing a full dependence surface while guarding against leverage points that can distort standard QQ estimates.	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.