DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models

Risultato della ricerca: Otherpeer review

Abstract

Many clinical and epidemiological studies rely on survival modelling to detect clinically relevant factors that affect various event histories. With the introduction of high-throughput technologies in the clinical and even large-scale epidemiological studies, the need for inference tools that are able to deal with fat data-structures, i.e., relatively small number of observations compared to the number of features, is becoming more prominent. This paper will introduce a principled sparse inference methodology for proportional hazards modelling, based on differential geometrical analyses of the high-dimensional likelihood surface.
Lingua originaleEnglish
Numero di pagine7
Stato di pubblicazionePublished - 2014

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