High dimensional regression on serum analytes

Yuanzhang Li, Emanuel Schwarz, Sabine Bahn, Robert H Yolken, David W. Niebuhr

Research output: Contribution to journalArticle

Abstract

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

Original languageEnglish (US)
JournalItalian Journal of Public Health
Volume9
Issue number4
DOIs
StatePublished - 2012

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Principal Component Analysis
Statistical Factor Analysis
Serum

Keywords

  • Gradient
  • High dimensional regression
  • Schizophrenia

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health Policy
  • Epidemiology
  • Community and Home Care

Cite this

High dimensional regression on serum analytes. / Li, Yuanzhang; Schwarz, Emanuel; Bahn, Sabine; Yolken, Robert H; Niebuhr, David W.

In: Italian Journal of Public Health, Vol. 9, No. 4, 2012.

Research output: Contribution to journalArticle

Li, Yuanzhang ; Schwarz, Emanuel ; Bahn, Sabine ; Yolken, Robert H ; Niebuhr, David W. / High dimensional regression on serum analytes. In: Italian Journal of Public Health. 2012 ; Vol. 9, No. 4.
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