পদ্ধতির তুলনা করুন

	দৃঢ় রিগ্রেশন	ল্যাসো রিগ্রেশন	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন	কোয়ান্টাইল রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	যন্ত্র শিখন	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Machine learning	Regression model	Regression model
উদ্ভবের বছর	1964	1996	2019	1978
প্রবর্তক	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
ধরন	Regression with outlier resistance	Regularized linear regression (L1 penalty)	Linear regression	Conditional quantile regression
মৌলিক উৎস	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
অপর নাম	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
সম্পর্কিত	6	4	5	5
সারসংক্ষেপ	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.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	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).	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.