0000000000443263

AUTHOR

F Abegaz

showing 2 related works from this author

Sparse relative risk survival modelling

2016

Cancer survival is thought to closed linked to the genimic constitution of the tumour. Discovering such signatures will be useful in the diagnosis of the patient and may be used for treatment decisions and perhaps even the development of new treatments. However, genomic data are typically noisy and high-dimensional, often outstripping the number included in the study. Regularized survival models have been proposed to deal with such scenary. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and (near) non-convex regularizer.

gene expression datarelative risk regression modelsurvival analysisparsityhigh dimensional datadifferential geometrydglarsSettore SECS-S/01 - Statistica
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DgCox: a differential geometric approach for high-dimensional Cox proportional hazard models

2014

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.

Proportional hazard modelling least angle regression differential geometry sparse inferenceSettore SECS-S/01 - Statistica
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