Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| BCa Bootstrap (bias-korrigerad och accelererad)× | Bayesiansk bootstrap (Rubin)× | Permutationstest (Randomiseringstest)× | |
|---|---|---|---|
| Ämnesområde | Statistik | Statistik | Statistik |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1987 | 1981 | 2005 |
| Upphovsperson≠ | Bradley Efron | Rubin (1981); large-sample theory by Lo (1987) | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Typ≠ | Resampling confidence interval | Resampling / posterior simulation | Nonparametric resampling test |
| Ursprungskälla≠ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Alias≠ | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples. | The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated. | 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. |
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