Bandingkan kaedah

	Bootstrap DEA: Pembetulan Ralat dan Selang Kebenaran untuk Skor Kecekapan	Inferens Bootstrap	Analisis Penyelidikan Data Jaringan (Network DEA)
Bidang	Analisis Kecekapan	Statistik	Analisis Kecekapan
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1998	1979	2000
Pengasas	Simar & Wilson	Bradley Efron	Färe & Grosskopf
Jenis	Nonparametric efficiency estimation with bootstrap inference	Resampling-based inference	Multi-stage nonparametric efficiency model
Sumber perintis	Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Alias	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Berkaitan	2	5	2
Ringkasan	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.	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.