The robust estimation of autoregressive processes by functional least squares

Author:

Heathcote C. R.,Welsh A. H.

Abstract

The stationary autoregressive model but with a long-tailed error distribution is analysed using the method of functional least squares. A family of estimators indexed by a real parameter is obtained and uniform consistency and weak convergence established. The optimum member of the family is chosen to have minimum variance with respect to the parameter, and the parameter value chosen detects and adjusts for long-tailed error distributions. Results of a simulation are given.

Publisher

Cambridge University Press (CUP)

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bootstrap Procedures for Online Monitoring of Changes in Autoregressive Models;Communications in Statistics - Simulation and Computation;2015-06-05

2. Extremes of Gaussian Processes;Laws of Small Numbers: Extremes and Rare Events;2010-09-30

3. Fourier Methods for Sequential Change Point Analysis in Autoregressive Models;Proceedings of COMPSTAT'2010;2010

4. Optimal Design Approach to GMM Estimation of Parameters Based on Empirical Transforms;International Statistical Review;2008-12

5. Finite-sample performance of alternative estimators for autoregressive models in the presence of outliers;Computational Statistics & Data Analysis;1999-09

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