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  Partner: UNT Libraries
 Department: Department of Information Technology and Decision Science
 Decade: 1990-1999
 Degree Level: Doctoral
 Collection: UNT Theses and Dissertations
Developing Criteria for Extracting Principal Components and Assessing Multiple Significance Tests in Knowledge Discovery Applications

Developing Criteria for Extracting Principal Components and Assessing Multiple Significance Tests in Knowledge Discovery Applications

Date: August 1999
Creator: Keeling, Kellie Bliss
Description: With advances in computer technology, organizations are able to store large amounts of data in data warehouses. There are two fundamental issues researchers must address: the dimensionality of data and the interpretation of multiple statistical tests. The first issue addressed by this research is the determination of the number of components to retain in principal components analysis. This research establishes regression, asymptotic theory, and neural network approaches for estimating mean and 95th percentile eigenvalues for implementing Horn's parallel analysis procedure for retaining components. Certain methods perform better for specific combinations of sample size and numbers of variables. The adjusted normal order statistic estimator (ANOSE), an asymptotic procedure, performs the best overall. Future research is warranted on combining methods to increase accuracy. The second issue involves interpreting multiple statistical tests. This study uses simulation to show that Parker and Rothenberg's technique using a density function with a mixture of betas to model p-values is viable for p-values from central and non-central t distributions. The simulation study shows that final estimates obtained in the proposed mixture approach reliably estimate the true proportion of the distributions associated with the null and nonnull hypotheses. Modeling the density of p-values allows for better control of ...
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