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Shape Constrained Non-parametric Estimators of the Baseline Distribution in Cox Proportional Hazards Model


Hendrik P. Lopuhaä, Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628CD, Delft, The Netherlands.


Abstract.  We investigate non-parametric estimation of a monotone baseline hazard and a decreasing baseline density within the Cox model. Two estimators of a non-decreasing baseline hazard function are proposed. We derive the non-parametric maximum likelihood estimator and consider a Grenander type estimator, defined as the left-hand slope of the greatest convex minorant of the Breslow estimator. We demonstrate that the two estimators are strongly consistent and asymptotically equivalent and derive their common limit distribution at a fixed point. Both estimators of a non-increasing baseline hazard and their asymptotic properties are obtained in a similar manner. Furthermore, we introduce a Grenander type estimator for a non-increasing baseline density, defined as the left-hand slope of the least concave majorant of an estimator of the baseline cumulative distribution function, derived from the Breslow estimator. We show that this estimator is strongly consistent and derive its asymptotic distribution at a fixed point.

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