Principal component analysis (PCA) is a widely used statistical technique for determining
subscales in questionnaire data. As in any other statistical technique, missing data may both …

Missing data are prevalent in many data analytic situations. Those in which principal
component analysis (PCA) is applied are no exceptions. The performance of five methods …

The basic technique of PCA has been generalized or adapted in many ways, and some
have already been discussed, in particular in Chapter 11 where adaptations for special …

Multiple imputation is one of the most highly recommended procedures for dealing with
missing data. However, to date little attention has been paid to methods for combining the …

A metric known as the total variance (more strictly the total sample variance) gives an idea of
the variation in data by summing the sample variance over each dimension. Assuming the …

Principal component analysis (also known as principal components analysis)(PCA) is a
technique from statistics for simplifying a data set. It was developed by Pearson (1901) and …

An important problem in principal component analysis (PCA) is the estimation of the correct
number of components to retain. PCA is most often used to reduce a set of observed …

When large multivariate datasets are analyzed, it is often desirable to reduce their
dimensionality. Principal component analysis is one technique for doing this. It replaces the …

抄録 Missing data arise in virtually all practical data analysis situations. The problem of how
to deal with them presents a major challenge to many data analysts. A variety of methods …

In this paper, the statistical significance of the contribution of variables to the principal
components in principal components analysis (PCA) is assessed nonparametrically by the …