You limited your search to:

  Partner: UNT Libraries
 Decade: 1990-1999
 Degree Discipline: Management Science
 Degree Level: Doctoral
 Collection: UNT Theses and Dissertations
Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

Classification by Neural Network and Statistical Models in Tandem: Does Integration Enhance Performance?

Date: December 1998
Creator: Mitchell, David
Description: The major purposes of the current research are twofold. The first purpose is to present a composite approach to the general classification problem by using outputs from various parametric statistical procedures and neural networks. The second purpose is to compare several parametric and neural network models on a transportation planning related classification problem and five simulated classification problems.
Contributing Partner: UNT Libraries
Comparing the Powers of Several Proposed Tests for Testing the Equality of the Means of Two Populations When Some Data Are Missing

Comparing the Powers of Several Proposed Tests for Testing the Equality of the Means of Two Populations When Some Data Are Missing

Date: May 1994
Creator: Dunu, Emeka Samuel
Description: In comparing the means .of two normally distributed populations with unknown variance, two tests very often used are: the two independent sample and the paired sample t tests. There is a possible gain in the power of the significance test by using the paired sample design instead of the two independent samples design.
Contributing Partner: UNT Libraries
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 ...
Contributing Partner: UNT Libraries
The Effect of Certain Modifications to Mathematical Programming Models for the Two-Group Classification Problem

The Effect of Certain Modifications to Mathematical Programming Models for the Two-Group Classification Problem

Date: May 1994
Creator: Wanarat, Pradit
Description: This research examines certain modifications of the mathematical programming models to improve their classificatory performance. These modifications involve the inclusion of second-order terms and secondary goals in mathematical programming models. A Monte Carlo simulation study is conducted to investigate the performance of two standard parametric models and various mathematical programming models, including the MSD (minimize sum of deviations) model, the MIP (mixed integer programming) model and the hybrid linear programming model.
Contributing Partner: UNT Libraries
A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications

A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications

Date: December 1993
Creator: Nam, Kyungdoo T.
Description: This study develops a heuristic procedure for specifying parameters for a neural network configuration (learning rate, momentum, and the number of neurons in a single hidden layer) in Shewhart X-bar control chart applications. Also, this study examines the replicability of the neural network solution when the neural network is retrained several times with different initial weights.
Contributing Partner: UNT Libraries
Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale

Robustness of Parametric and Nonparametric Tests When Distances between Points Change on an Ordinal Measurement Scale

Date: August 1994
Creator: Chen, Andrew H. (Andrew Hwa-Fen)
Description: The purpose of this research was to evaluate the effect on parametric and nonparametric tests using ordinal data when the distances between points changed on the measurement scale. The research examined the performance of Type I and Type II error rates using selected parametric and nonparametric tests.
Contributing Partner: UNT Libraries