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| Vanligaste minsta kvadratmetoden (OLS) Regression× | Permutationstest (Randomiseringstest)× | Theil-Sen Estimator× | |
|---|---|---|---|
| Ämnesområde≠ | Ekonometri | Statistik | Statistik |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 2019 | 2005 | 1968 |
| Upphovsperson≠ | Wooldridge (textbook treatment); classical least squares | Good (2005); Edgington & Onghena (2007); resampling tradition | Henri Theil (1950); P. K. Sen (1968) |
| Typ≠ | Linear regression | Nonparametric resampling test | Robust linear regression |
| Ursprungskälla≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ |
| Alias≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | randomization test, exact permutation test, re-randomization test, Permütasyon Testi | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator |
| Närliggande≠ | 5 | 5 | 6 |
| Sammanfattning≠ | 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). | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. |
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