A simulation-approximation approach to sample size planning for high-dimensional classification studies.

ABSTRACT: Classification studies with high-dimensional measurements and relatively small sample sizes are increasingly common. Prospective analysis of the role of sample sizes in the performance of such studies is important for study design and interpretation of results, but the complexity of typical pattern discovery methods makes this problem challenging. The approach developed here combines Monte Carlo methods and new approximations for linear discriminant analysis, assuming multivariate normal distributions. Monte Carlo methods are used to sample the distribution of which features are selected for a classifier and the mean and variance of features given that they are selected. Given selected features, the linear discriminant problem involves different distributions of training data and generaliz

SUBMITTER: de Valpine P 

PROVIDER: S-EPMC2697341 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature