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Time series AR(1) model for short-tailed distributions
Date
2005-04-01
Author
Akkaya, AD
Tiku, ML
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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The innovations in AR(1) models in time series have primarily been assumed to have a normal or long-tailed distributions. We consider short-tailed distributions (kurtosis less than 3) and derive modified maximum likelihood (MML) estimators. We show that the MML estimator of 0 is considerably more efficient than the commonly used least squares estimator and is also robust. This paper is essentially the first to achieve robustness to inliers and to various forms of short-tailedness in time series analysis.
Subject Keywords
Statistics, Probability and Uncertainty
,
Statistics and Probability
URI
https://hdl.handle.net/11511/64932
Journal
STATISTICS
DOI
https://doi.org/10.1080/02331880500031407
Collections
Department of Statistics, Article
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A. Akkaya and M. Tiku, “Time series AR(1) model for short-tailed distributions,”
STATISTICS
, pp. 117–132, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64932.