Comparar métodos

	Bootstrap Paramétrico	Bootstrap BCa (Correção de Viés e Aceleração)	Inferência Bootstrap
Área	Estatística	Estatística	Estatística
Família	Regression model	Regression model	Regression model
Ano de origem	1993	1987	1979
Autor original	Efron & Tibshirani; Davison & Hinkley	Bradley Efron	Bradley Efron
Tipo	Resampling-based inference (model-based)	Resampling confidence interval	Resampling-based inference
Fonte seminal	Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. CRC Press. ISBN: 978-0412042317	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.
Outros nomes	parametrik bootstrap, model-based bootstrap, parametric resampling	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Relacionados	5	5	5
Resumo	The parametric bootstrap is a resampling method that estimates standard errors and confidence intervals by drawing repeated samples from a parametric model that has been fitted to the data. Developed in the bootstrap literature of Efron and Tibshirani (1993) and Davison and Hinkley (1997), it replaces analytic derivations for non-normal distributions and complex statistics.	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.