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- W1489771850 abstract "<!-- *** Custom HTML *** --> In a simple measurement error regression model, the classical least squares estimator of the slope parameter consistently estimates a discounted slope, though sans normality, some other properties may not hold. It is shown that for a broader class of error distributions, the Theil–Sen estimator, albeit nonlinear, is a median-unbiased, consistent and robust estimator of the same discounted parameter. For a general class of nonlinear (including <i>R</i>–, <i>M</i>– and <i>L</i>– estimators), study of asymptotic properties is greatly facilitated by using some uniform asymptotic linearity results, which are, in turn, based on contiguity of probability measures. This contiguity is established in a measurement error model under broader distributional assumptions. Some asymptotic properties of the Theil–Sen estimator are studied under slightly different regularity conditions in a direct way bypassing the contiguity approach." @default.
- W1489771850 created "2016-06-24" @default.
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- W1489771850 date "2010-01-01" @default.
- W1489771850 modified "2023-10-16" @default.
- W1489771850 title "The Theil–Sen estimator in a measurement error perspective" @default.
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- W1489771850 doi "https://doi.org/10.1214/10-imscoll722" @default.
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