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The need to understand large database structures is an important issue in biological and medical science. This review paper is aimed at quantitative medical researchers looking for guidance in modeling large numbers of variables in medical research, how this relates to standard linear models and the geometry that underlies their analysis. Issues reviewed include LASSO-related approaches, principal-component based analysis, and issues of model stability and interpretation. Model misspecification issues related to potential nonlinearities are also examined, as is the Bayesian perspective on these issues.
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Michael Brimacombe (2014) studied this question.
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