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Bootstrap-inferentie×Robuuste tijdreeksanalyse×
VakgebiedStatistiekStatistiek
FamilieRegression modelRegression model
Jaar van ontstaan19792019
GrondleggerBradley EfronMaronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition
TypeResampling-based inferenceRobust time series model (AR / MA / ARIMA)
Oorspronkelijke bronEfron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687
Aliassenbootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımırobust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi
Verwant55
SamenvattingBootstrap 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.Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019).
ScholarGateGegevensset
  1. v1
  2. 2 Bronnen
  3. PUBLISHED
  1. v1
  2. 2 Bronnen
  3. PUBLISHED

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ScholarGateMethoden vergelijken: Bootstrap Inference · Robust Time Series Analysis. Geraadpleegd op 2026-06-17 via https://scholargate.app/nl/compare