Variable Selection for Compositional Data

Covariance - based variable selection for compositional data

by Karel Hron23, Peter Filzmoser4, Sandra Donevska23 and Eva Fǐserová23 1 Received ....................; accepted .......................... 2 Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, 17. listopadu 12, 771 46 Olomouc, Czech Republic; e-mail: [email protected], [email protected], [email protected] 3 Department of Geoinformati...

Variable selection for multivariate failure time data.

In this paper, we proposed a penalised pseudo-partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients. Under certain regularity conditions, we show the consistency and asymptotic normality of the penalised likelihood estimators. We further demonstrate that, for certain penalty functions with proper choices of regulari...

High-Dimensional Variable Selection for Survival Data

The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a...

Variable Selection for Multivariate Survival data

It is assumed for the Cox’s proportional hazards model that the survival times of subjects are independent. This assumption might be violated in some situations, in which the collected data are correlated. The well-known Cox model is not valid in this situation because independence assumption among individuals is violated. For this purpose Cox’s proportional hazard model is extent to the analys...

Variable selection for multifactorial genomic data