The Annals of Statistics

Robust Interval Estimation of the Innovation Variance of an Arma Model

William W. Davis

Full-text: Open access

Abstract

For the autoregressive-moving average time series model, the normal theory procedure for setting confidence intervals for the error variance is not robust against nonnormality. This paper proposes three asymptotically robust techniques: they are a "standard-error" procedure, an analog of Box's simple data splitting technique, and the jackknife procedure. The large sample distribution of each of these techniques is derived.

Article information

Source
Ann. Statist., Volume 5, Number 4 (1977), 700-708.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176343893

Digital Object Identifier
doi:10.1214/aos/1176343893

Mathematical Reviews number (MathSciNet)
MR436500

Zentralblatt MATH identifier
0361.62079

JSTOR
links.jstor.org

Subjects
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 62G35: Robustness

Keywords
Autoregressive-moving average discrete time series variance data splitting jackknife robust inference

Citation

Davis, William W. Robust Interval Estimation of the Innovation Variance of an Arma Model. Ann. Statist. 5 (1977), no. 4, 700--708. doi:10.1214/aos/1176343893. https://projecteuclid.org/euclid.aos/1176343893


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