Compara mètodes

	Bootstrap DEA	Inferencia Bootstrap
Camp	Anàlisi d'eficiència	Estadística
Família	Regression model	Regression model
Any d'origen	1998	1979
Autor original	Simar & Wilson	Bradley Efron
Tipus	Nonparametric efficiency estimation with bootstrap inference	Resampling-based inference
Font seminal	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.
Àlies	Bootstrapped DEA, DEA Bootstrap Inference, Simar-Wilson Bootstrap, Bootstrap Sınır Analizi	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Relacionats	2	5
Resum	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.