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.
Wit, E., Augugliaro, L., Abegaz, F., Gonzalez, J. (2014). DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models. In Proceedings of the Eleventh international Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics.
DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models
AUGUGLIARO, Luigi;
2014-01-01
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.File | Dimensione | Formato | |
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