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| 네트워크 자료포괄분석 (Network DEA)× | 부트스트랩 DEA: 효율성 점수에 대한 편향 보정 및 신뢰 구간× | |
|---|---|---|
| 분야 | 효율성 분석 | 효율성 분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2000 | 1998 |
| 창시자≠ | Färe & Grosskopf | Simar & Wilson |
| 유형≠ | Multi-stage nonparametric efficiency model | Nonparametric efficiency estimation with bootstrap inference |
| 원전≠ | Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. DOI ↗ | 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 ↗ |
| 별칭 | Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi | Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi |
| 관련 | 2 | 2 |
| 요약≠ | Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model. | 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. |
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