পদ্ধতির তুলনা করুন

	Bayesian Robust Regression	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1993	2019
প্রবর্তক	Geweke (1993); Gelman et al. (2013)	Wooldridge (textbook treatment); classical least squares
ধরন	Bayesian regression with heavy-tailed errors	Linear regression
মৌলিক উৎস	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	Bayesian heavy-tailed regression, Bayesian Student-t regression, robust Bayesian linear model, BRR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	6	5
সারসংক্ষেপ	Bayesian Robust Regression replaces the Gaussian error assumption of ordinary linear regression with a heavy-tailed distribution — most commonly the Student-t — and estimates all parameters in a Bayesian framework. The heavier tails give outliers less influence on the fitted line, yielding stable coefficient estimates and honest uncertainty intervals even when the data contain unusual observations.	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).