Univariate Shrinkage in the Cox Model for High Dimensional Data

Univariate shrinkage in the cox model for high dimensional data.

We propose a method for prediction in Cox's proportional model, when the number of features (regressors), p, exceeds the number of observations, n. The method assumes that the features are independent in each risk set, so that the partial likelihood factors into a product. As such, it is analogous to univariate thresholding in linear regression and nearest shrunken centroids in classification. ...

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Shrinkage Estimators for High-Dimensional Covariance Matrices

As high-dimensional data becomes ubiquitous, standard estimators of the population covariance matrix become difficult to use. Specifically, in the case where the number of samples is small (large p small n) the sample covariance matrix is not positive definite. In this paper we explore some recent estimators of sample covariance matrices in the large p, small n setting namely, shrinkage estimat...

Robust High - Dimensional Data Analysis Using a Weight Shrinkage

Cox model in high dimensional and low sample size settings

The evolution of cancer is more certainly linked to a complex interplay of genes rather than a single gene activity. Multivariate analysis, which can exploit the correlated pattern of gene expression display by genes behaving jointly, such as genes performing the same functions or genes operating along the same pathway, can become a very useful diagnostic tool to determine molecular predictor o...