Measure of location-based estimators in simple linear regression
In this paper we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least...
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2015
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| Online Access: | https://ink.library.smu.edu.sg/soe_research/2329 https://ink.library.smu.edu.sg/context/soe_research/article/3328/viewcontent/FULLTEXT01.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this paper we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error are assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroscedastic regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study. |
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