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This paper develops detailed mathematical statistical theory of a new classof cross-validation techniques of local linear kernel hazards and theirmultiplicative bias corrections. The new class of cross-validation combinesprinciples of local information and recent advances in indirectcross-validation. A few applications of cross-validating multiplicative kernelhazard estimation do exist in the literature. However, detailed mathematicalstatistical theory and small sample performance are introduced via this paperand further upgraded to our new class of best one-sided cross-validation. Bestone-sided cross-validation turns out to have excellent performance in itspractical illustrations, in its small sample performance and in itsmathematical statistical theoretical performance.

Multiplicative local linear hazard estimation and best one-sided cross-validation