Semiparametric inference methods for general time scale models.
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.
Department of Statistics, University of Toronto, Toronto, ON, Canada, M5S 3G3. duchesne@utstat.utoronto.ca
Thierry
T
Duchesne
Jerry
J
Lawless
D002170:Canada; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D018570:Risk Assessment; D016019:Survival Analysis; D013995:Time