The present investigation was a Monte Carlo experiment designed to evaluate the performance of several metrics in spotting correlational outliers. Specifically, the metrics that were compared were the Mahalanobis D², Bacon MLD, Carrig D, MCD, Robust PCLOW and Robust PCHIGH. This was the first comparative simulation study to include robust PCLOW and robust PCHIGH. The Mahalanobis D², MCD, Robust PCLOW and Robust PCHIGH were each applied using an approximate statistical criterion. The Carrig D and Bacon MLD were applied using a "natural drop" approach that separated scores on the metric into two groups: outlying and non-outlying. The "natural drop" utilizes a k-means algorithm from cluster analysis to separate the scores into the two groups. Both majority and contaminant observations were generated from multivariate normal distributions based on factor-analytic models. Experimental factors included majority versus contaminant communality level, majority-contaminant factor models scenario, number of variables, sample size and fraction of outliers. Results indicated that the "natural drop" method of application for the Carrig D and Bacon MLD leads to intolerably high false-alarm rates. Overall, PCLOW clearly outperformed PCHIGH. Suprisingly, PCLOW did not distinguish itself from MCD in terms of performance as expected in certain experimental conditions. The conditions in this study were limited. Future comparative studies of the metrics could include conditions of non-normality and hybrid types of outliers (i.e. outliers that are both mean shift and correlational). Despite its poor performance in this study, I theorize that robust PCHIGH could have an advantage over MCD in spotting certain kinds of mean-shift outliers. Also, research into the distributional properties of the Carrig D is warranted. / text

A wide array of procedures have been proposed for testing correlation pattern. Many, but not all, of the statistical techniques available for testing correlation pattern are derived under the distributional condition of multivariate normality which does not always hold in the behavioral, educational and social sciences. Though a number of studies have explored the performance of structure analysis techniques under conditions of multivariate nonnormality, very little is known about the actual performance of many correlation structure analysis techniques under conditions of multivariate nonnormality. In addition, very little is known about the actual concurrent performance of tests of multivariate normality. The present investigation ascertains how tests of correlation pattern hypotheses and indicators of multivariate normality perform when data are from multivariate normal or nonnormal parent populations. This paper reviews and examines, using a Monte Carlo simulation study, the concurrent performance of different approaches to testing (1) correlation pattern hypotheses, including, (i) normal theory (NT) and asymptotically distribution free (ADF) covariance structure analysis techniques, (ii) NT and ADF correlation structure analysis techniques, (iii) correlation pattern specific techniques; (2) the distributional assumption of multivariate normality using statistics based on Mardia's measures of multivariate skewness and kurtosis. This paper also examines the performance characteristics of test procedures based on joint consideration of tests of multivariate normality and structure analysis techniques. Performance of the covariance and correlation structure analysis techniques, tests of multivariate normality, and joint test procedures was assessed across different types of correlation pattern models, numbers of variables, levels of skew and kurtosis, sample sizes, and nominal alpha levels, on the primary Neyman-Pearson criterion for an optimal test, according to which an optimal procedure (1) controls experimentwise Type I error rate at or below the nominal level, (2) maximizes power.

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The steps of theoretical aspects of univariate, adjusted univariate and multivariate analysis for repeated measures studies are presented. Some formulas of the test procedures are extended to make computation easy. As a verification and to provide background experience of the test procedures, some recent data from livestock, clinical, psychological and physiological experiments are analysed and the results are incorporated with the theoretical discussion. Considering the repeated measurements experiments model several wellknown procedures are presented for multiple inferences of the occasion contrasts. The adjusted and multivariate tests for multiple comparisons of repeated measures means are also considered when the sphericity requirements are not considered valid. A simulation study is made to compare the univariate, adjusted univariate and multivariate methods. The sample variance-covariance matrices observed from a number of experimental studies as representative of population variance-covariance patterns are used to generate sets of multivariate normal 'lq observation. The sphericity and homogeneity assumptions of the matrices are presented and the comparisons are interpreted in the light of these assumptions. Actual (fractional) degrees of freedom of the test statistics are used and an investigation is then presented which compares several data analytic strategies when the assumptions are violated. The question of how to analyse repeated measures data with missing observations is a common problem facing analysts. An attractive approach is to estimate the missing values and then proceed with the standard analysis. A new method for values which are missing at random is discussed and compared with other methods, and the new method found best.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2008. / Thesis Advisor(s): Buttrey, Samuel E. "June 2008." Description based on title screen as viewed on August 27, 2008. Includes bibliographical references (p.133-134). Also available in print.

Thesis (Ph. D.)--University of Washington, 1992. / Vita. Includes bibliographical references (leaves [164]-168).

Thesis (M.S. in Management)--Naval Postgraduate School, March 2009. / Thesis Advisor(s): Abdel-Hamid, Tarek ; Ferrer, Geraldo. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Manpower Forecasting; Brazilian Air Force Health System Manpower; Systems Dynamics; System Thinking; Modeling and Simulation; Multivariate Analysis. Includes bibliographical references (p. 65-66). Also available in print.

Thesis (M.S. in Management)--Naval Postgraduate School, December 1990. / Thesis Advisor(s): Gorman, Linda ; Roberts, Benjamin J. "December 1990." Description based on title screen as viewed on March 30, 2010. DTIC Descriptor(s): Retention(General), Mathematical Models, Measurement, Navy, Theses, Regression Analysis, Medicine, Limitations, Logistics, Motivation, Patients, Workload, Hospitals, Naval Shore Facilities, Shortages, Nurses, Nursing. DTIC Identifier(s): Nurse, Nursing Shortage, Navy Nurse Corps, Turnover, Retention, Multivariate Analysis, Logistic Regression. Author(s) subject terms: Nurse, Nursing Shortage, Retention, Multivariate Analysis, Logistic Regression, Navy Nurse Corps, Turnover. Includes bibliographical references (p. 85-89). Also available in print.