قارن الطرق

	إعادة أخذ العينات بالجاك نايف	انحدار الكميات (الصيغ غير المعلمية)	انحدار المربعات الصغرى العادية (OLS)
المجال	الإحصاء	الإحصاء	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	1956	1978	2019
صاحب الطريقة	Quenouille (1956); reviewed by Miller (1974)	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares
النوع	Resampling / bias and variance estimation	Quantile regression (nonparametric variants)	Linear regression
المصدر التأسيسي	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
الأسماء البديلة	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5	5
الملخص	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).