Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Bayesian Bootstrap (Rubin)× | Bootstrap ya BCa (iliyorekebishwa kwa upendeleo na kuharakishwa)× | Utoaji wa Hitimisho kwa Njia ya Bootstrap× | |
|---|---|---|---|
| Nyanja | Takwimu | Takwimu | Takwimu |
| Familia | Regression model | Regression model | Regression model |
| Mwaka wa asili≠ | 1981 | 1987 | 1979 |
| Mwanzilishi≠ | Rubin (1981); large-sample theory by Lo (1987) | Bradley Efron | Bradley Efron |
| Aina≠ | Resampling / posterior simulation | Resampling confidence interval | Resampling-based inference |
| Chanzo asilia≠ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| Majina mbadala≠ | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| Zinazohusiana | 5 | 5 | 5 |
| Muhtasari≠ | 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 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. | 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. |
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