High Dimensional Regression on Serum Analytes (Italian Journal of Public Health, 2012)
Regression of high dimensional data is particularly difficult when the number of observations is limited. Principal Component Analysis, canonical correlation analysis and factor analysis are commonly used methods to reduce data dimensions, but usually cannot find the most significant linear combination. The goal is usually to find a particular partition of the space X consisting of all independent factors. In this paper, we propose an approach to high dimensional regression for applications where N>K or N
Keywords: Gradient; High dimensional regression; Schizophrenia