পদ্ধতির তুলনা করুন

	RANSAC রিগ্রেশন	Least Trimmed Squares (LTS) Regression	কোয়ান্টাইল রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1981	1984	1978
প্রবর্তক	Fischler & Bolles	Peter J. Rousseeuw	Koenker & Bassett
ধরন	Robust linear regression	Robust linear regression	Conditional quantile regression
মৌলিক উৎস	Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
অপর নাম	random sample consensus, RANSAC, robust regression, RANSAC Regresyonu	LTS, least trimmed squares regression, trimmed least squares, robust regression	conditional quantile regression, regression quantiles, Kantil Regresyon
সম্পর্কিত	5	5	5
সারসংক্ষেপ	RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	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.