विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बूस्टस्ट्रैप डीईए: दक्षता स्कोर के लिए पूर्वाग्रह सुधार और विश्वास अंतराल× | बूटस्ट्रैप अनुमान× | |
|---|---|---|
| क्षेत्र≠ | दक्षता विश्लेषण | सांख्यिकी |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1998 | 1979 |
| प्रवर्तक≠ | Simar & Wilson | Bradley Efron |
| प्रकार≠ | Nonparametric efficiency estimation with bootstrap inference | Resampling-based inference |
| मौलिक स्रोत≠ | Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ |
| उपनाम | Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı |
| संबंधित≠ | 2 | 5 |
| सारांश≠ | Bootstrap Data Envelopment Analysis (Bootstrap DEA) is a resampling-based extension of standard DEA that provides statistically valid inference for efficiency scores. Introduced by Simar and Wilson in 1998, it addresses the core weakness of classical DEA — its inability to quantify uncertainty in estimated scores — by constructing bootstrap confidence intervals and bias-corrected efficiency estimates from repeatedly resampled pseudo-frontiers. | 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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