Regression model
稳健时间序列分析
稳健时间序列分析使用 M 估计或 MM 估计代替普通最小二乘法，对包含异常值或结构性断裂的时间序列拟合自回归、移动平均和 ARIMA 模型，从而避免少数异常观测值扭曲拟合结果。它遵循 Maronna、Martin、Yohai 和 Salibián-Barrera (2019) 巩固的稳健统计学传统。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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
- Peña, D., & Guttman, I. (1988). A Bayesian Approach for Predicting with Outliers. Journal of the American Statistical Association. link ↗
如何引用本页
ScholarGate. (2026, June 1). Robust Time Series Analysis (M- and MM-estimation based AR / MA / ARIMA). ScholarGate. https://scholargate.app/zh/statistics/robust-time-series
Which method?
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.
- 断点分析统计学↔ compare
- 中位数绝对离差 (MAD) 估计统计学↔ compare
- 普通最小二乘法 (OLS) 回归计量经济学↔ compare
- 稳健线性混合效应模型统计学↔ compare
- Sn和Qn稳健尺度估计量统计学↔ compare