Comparar métodos

	Reamostragem Jackknife	Inferência Bootstrap	Regressão por Mínimos Quadrados Ordinários (MQO)	Teste de Permutação (Randomização)
Área	Estatística	Estatística	Econometria	Estatística
Família	Regression model	Regression model	Regression model	Regression model
Ano de origem	1956	1979	2019	2005
Autor original	Quenouille (1956); reviewed by Miller (1974)	Bradley Efron	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipo	Resampling / bias and variance estimation	Resampling-based inference	Linear regression	Nonparametric resampling test
Fonte seminal	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Outros nomes	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Relacionados	5	5	5	5
Resumo	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.