Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Fisher Exact Randomization Inference× | Bootstrap Inference× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|---|
| Nozare≠ | Statistika | Statistika | Ekonometrija |
| Saime | Regression model | Regression model | Regression model |
| Izcelsmes gads≠ | 1935 | 1979 | 2019 |
| Autors≠ | Ronald A. Fisher | Bradley Efron | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Exact permutation-based inference | Resampling-based inference | Linear regression |
| Pirmavots≠ | Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd. link ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Citi nosaukumi | fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization) | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās | 5 | 5 | 5 |
| Kopsavilkums≠ | Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions. | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | 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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