Abstract
The use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.
Highlights
This paper applies model selection criteria, especially AIC and BIC, to the problem of choosing a sufficient number of principal components to retain
To begin the discussion here, we first give a short review of some general background on the relevant portions of multivariate statistical analysis, such as may be obtained from textbooks such as Anderson [5] or Johnson and Wichern [9]
The variables include Age, Systolic blood pressure, Diastolic blood pressure, weight, height and Coronary Incident, a binary variable indicating whether or not the individual had a coronary incident during the course of the study
Summary
This paper applies model selection criteria, especially AIC and BIC, to the problem of choosing a sufficient number of principal components to retain. It applies the concepts of Sclove [13] to this particular problem
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