An asymptotically optimal adaptive selection procedure in the proportional hazards model with conditionally independent censoring
Biometrical Journal, 40:8, 963–978 (1998)
Assume k independent populations are given which are distributed according to a one-dimensional parameter family. Taking samples of size n the population with the smallest parameter value is to be selected. Using the framework of Le Cam´s decision theory (Le Cam, 1986; Strasser, 1985) under mild regularity assumptions, an asymptotically optimal selection procedure is derived for the sequence of localized models. In the proportional hazards model with conditionally independent censoring, an asymptotically optimal adaptive selection procedure is constructed by substituting the unknown nuisance parameter by a kernel estimator.