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ARIMA (Autoregressive Integrated Moving Average) Modell×Bootstrap-inferens×
FagfeltØkonometriStatistikk
FamilieRegression modelRegression model
Opprinnelsesår20151979
OpphavspersonBox & Jenkins (Box-Jenkins methodology)Bradley Efron
TypeUnivariate time-series modelResampling-based inference
Opprinnelig kildeBox, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗
AliasBox-Jenkins model, ARIMA(p,d,q), ARIMA Modelibootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı
Relaterte55
SammendragARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).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.
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ScholarGateSammenlign metoder: ARIMA · Bootstrap Inference. Hentet 2026-06-19 fra https://scholargate.app/no/compare