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Msaidizi
Regression model

Uchambuzi Imara wa Mfululizo wa Wakati

Uchambuzi Imara wa Mfululizo wa Wakati huweka miundo ya kiwango cha kiwango cha kurudi nyuma (autoregressive), uhamaji wa wastani (moving-average), na ARIMA kwenye mfululizo wenye alama za nje au vipindi vya kimuundo, kwa kutumia M-estimation au MM-estimation badala ya mbinu ya kawaida ya viwango vidogo vya mraba (ordinary least squares) ili uchache wa maangalizi yasiyo ya kawaida usipotoshe marekebisho. Inafuata mila ya takwimu imara iliyojumuishwa katika Maronna, Martin, Yohai na Salibián-Barrera (2019).

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Vyanzo

  1. 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
  2. Peña, D., & Guttman, I. (1988). A Bayesian Approach for Predicting with Outliers. Journal of the American Statistical Association. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 1). Robust Time Series Analysis (M- and MM-estimation based AR / MA / ARIMA). ScholarGate. https://scholargate.app/sw/statistics/robust-time-series

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateRobust Time Series Analysis (Robust Time Series Analysis (M- and MM-estimation based AR / MA / ARIMA)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/statistics/robust-time-series · Seti ya data: https://doi.org/10.5281/zenodo.20539026