Thesis (Ph. D.)--University of Hong Kong, 1995. / "June 1994." Includes bibliographical references (leave 149-161).

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 11, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. (Tony) Jianguo Sun. Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Florida, 1983. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaf 83).

Thesis (Ph.D.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.

No description available.

This report presents the descriptive data analysis and failure time modeling that can be used to find out the characteristics and pattern of failure time. Descriptive data analysis includes the mean, median, 1st quartile, 3rd quartile, frequency, standard deviation, skewness, kurtosis, minimum, maximum and range. Models like exponential distribution, gamma distribution, normal distribution, lognormal distribution, Weibull distribution and log-logistic distribution have been studied for failure time data. The data in this report comes from the South Texas Project that was collected during the last 40 years. We generated more than 1000 groups for STP failure time data based on Mfg Part Number. In all, the top twelve groups of failure time data have been selected as the study group. For each group, we were able to perform different models and obtain the parameters. The significant level and p-value were gained by Kolmogorov-Smirnov test, which is a method of goodness of fit test that represents how well the distribution fits the data. The In this report, Weibull distribution has been proved as the most appropriate model for STP dataset. Among twelve groups, eight groups come from Weibull distribution. In general, Weibull distribution is powerful in failure time modeling. / text

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015. / Exploratory data analysis (EDA) methods such as Principal Component Analysis (PCA) play an important role in statistical analysis. The analysis assumes that a complete dataset is observed. If the underlying data contains missing observations, the analysis cannot be completed immediately as a method to handle these missing observations must first be implemented. Missing data are a problem in any area of research, but researchers tend to ignore the problem, even though the missing observations can lead to incorrect conclusions and results. Many methods exist in the statistical literature for handling missing data. There are many methods in the context of PCA with missing data, but few studies have focused on a comparison of these methods in order to determine the most effective method. In this study the effectiveness of the Expectation Maximisation (EM) algorithm and the iterative PCA (iPCA) algorithm are assessed and compared against the well-known yet flawed methods of case-wise deletion (CW) and mean imputation. Two techniques for the application of the multiple imputation (MI) method of Markov Chain Monte Carlo (MCMC) with the EM algorithm in a PCA context are suggested and their effectiveness is evaluated compared to the other methods. The analysis is based on a simulated dataset and the effectiveness of the methods analysed using the sum of squared deviations (SSD) and the Rv coefficient, a measure of similarity between two datasets. The results show that the MI technique applying PCA in the calculation of the final imputed values and the iPCA algorithm are the most effective techniques, compared to the other techniques in the analysis.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries