Fusing microarray experiments with multivariate regression

Fusing microarray experiments with multivariate regression

MOTIVATION It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should...

Multivariate Regression with Calibration

We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...

Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments

PURPOSE To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer. METHODS A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points w...

Bayesian regression with multivariate linear splines

Multivariate convex regression with adaptive partitioning

We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research and financial engineering, but there is currently no multivariate method that is computationally feasible for more than a few hundred observations. We introduce Convex Adaptive Pa...