ScholarGate
Asystent

Porównaj metody

Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.

Estymacja bootstrapowa×Regresja metodą najmniejszych kwadratów (OLS)×Test permutacyjny (randomizacyjny)×
DziedzinaStatystykaEkonometriaStatystyka
RodzinaRegression modelRegression modelRegression model
Rok powstania197920192005
TwórcaBradley EfronWooldridge (textbook treatment); classical least squaresGood (2005); Edgington & Onghena (2007); resampling tradition
TypResampling-based inferenceLinear regressionNonparametric resampling test
Źródło pierwotneEfron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Inne nazwybootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımıordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonurandomization test, exact permutation test, re-randomization test, Permütasyon Testi
Pokrewne555
PodsumowanieBootstrap 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.
ScholarGateZbiór danych
  1. v1
  2. 2 Źródła
  3. PUBLISHED
  1. v1
  2. 1 Źródła
  3. PUBLISHED
  1. v1
  2. 2 Źródła
  3. PUBLISHED

Przejdź do wyszukiwania Pobierz slajdy

ScholarGatePorównaj metody: Bootstrap Inference · OLS Regression · Permutation Test. Pobrano 2026-06-17 z https://scholargate.app/pl/compare