Semiparametric Inference Methods for General Time Scale Models |
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| Authors: | Duchesne Thierry Lawless Jerry |
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| Affiliation: | (1) Department of Statistics, University of Toronto, Toronto, ON, Canada, M5S 3G3;(2) Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada, N2L 3G1 |
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| Abstract: | In this paper we consider semiparametric inference methods for the time scale parameters in general time scale models (Oakes, 1995, Duchesne and Lawless, 2000). We use the results of Robins and Tsiatis (1992) and Lin and Ying (1995) to derive a rank-based estimator that is more efficient and robust than the traditional minimum coefficient of variation (min CV) estimator of Kordonsky and Gerstbakh (1993) for many underlying models. Moreover, our estimator can readily handle censored samples, which is not the case with the min CV method. |
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| Keywords: | accelerated failure time model collapsible model generalized residuals ideal time scale minimum coefficient of variation linear rank estimator separable scale model unbiased estimating function |
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