পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ডব্লিউ-এস্টিমেটর রোবাস্ট রিগ্রেশন (ওয়েলশ / টুকে বাইস্কোয়ার)× | MM-Estimation for Robust Regression× | সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন× | |
|---|---|---|---|
| ক্ষেত্র≠ | পরিসংখ্যান | পরিসংখ্যান | অর্থমিতি |
| পরিবার | Regression model | Regression model | Regression model |
| উদ্ভবের বছর≠ | 1974 | 1987 | 2019 |
| প্রবর্তক≠ | Beaton & Tukey (bisquare weight); Welsch (Welsch weight) | Victor J. Yohai | Wooldridge (textbook treatment); classical least squares |
| ধরন≠ | Robust regression (redescending M-estimator) | Robust linear regression | Linear regression |
| মৌলিক উৎস≠ | Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics, 16(2), 147-185. DOI ↗ | Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| অপর নাম | Tukey bisquare M-estimator, Welsch M-estimator, redescending M-estimator, W-Tahmin Edici (Welsch / Tukey Bisquare) | MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| সম্পর্কিত≠ | 4 | 5 | 5 |
| সারসংক্ষেপ≠ | The W-estimator is a family of robust M-estimator variants for linear regression that use the Tukey bisquare and Welsch weight functions, introduced in the line of work going back to Beaton and Tukey (1974). Because its weights fall rapidly toward zero as a residual grows, it resists outliers more strongly than the Huber M-estimator. | The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved. | 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). |
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