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An integration of partial least squares and sparsified multiple correspondence analysis applied to genetic data

Year: 2014

Type: conference-paper

Abstract: Genetic data are now common in many domains. Typically, these genetic studies try to associate genetics withsingle phenotypes, behaviors, or diagnostic criteria. However, many of these studies include multiple behavioral variables and very large genetic data sets. The analysis of these data sets faces two particular challenges: 1) How to integrate many behavioral and genetic variables when 2) only a small number of variables are interpretable. To address these issues, we propose the integration of partial least squares and a sparsified approach to multiple correspondence analysis.

Source: HAL

Authors: Beaton, Derek, Bernard, Anne, Abdi, Hervé, Saporta, Gilbert

Institutions: The University of Texas at Dallas, Centre d'études et de recherche en informatique et communications

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3029060063

FWCI:

Cites: 7

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Related to: 10