Outlier detection tests based on martingale estimating equations for stochastic processes
DOI10.1016/0304-4149(94)90073-6zbMath0808.62075OpenAlexW2089984037MaRDI QIDQ1343589
Publication date: 19 January 1995
Published in: Stochastic Processes and their Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0304-4149(94)90073-6
sign testregression modelautoregressive processautoregressive errorsfunctions of estimated residualsoutlier detection testrobustified score test
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Robustness and adaptive procedures (parametric inference) (62F35) Non-Markovian processes: hypothesis testing (62M07)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Influence functionals for time series (with discussion)
- Optimal robust estimation for discrete time stochastic processes
- A note on strong consistency of least squares estimators in regression models with martingale difference errors
- On conditional least squares estimation for stochastic processes
- THE SIGN TEST FOR STOCHASTIC PROCESSES
- The foundations of finite sample estimation in stochastic processes
- Robust tests for time series with an application to first-order autoregressive processes
- Quasi-Likelihood and Optimal Estimation, Correspondent Paper
- Outlier-detection tests and robust estimators based on signs of residuals
- General M-estimates for contaminated p th-order autoregressive processes: Consistency and asymptotic normality
- A short-cut test for outliers using residuals
- Robust Estimation of the First-Order Autoregressive Parameter
- The Sample Mean Among the Moderate Order Statistics
This page was built for publication: Outlier detection tests based on martingale estimating equations for stochastic processes