Date of Graduation
Master of Science in Mathematics
multivariate normal distribution, hypothesis testing, likelihood ratio test, union intersection test, discriminant analysis, Fisher's discriminant function, maximum likelihood allocation rule
Analyzing large data sets is often time-consuming as many data sets depend on many variables, and multiple methods of analyzing such data sets are explored. In many practical situations such data sets can be modeled by the multivariate normal distribution. For statistical analysis of multivariate data sets, hypothesis testing and discriminant analysis are often used. These techniques require a strong background in univariate statistics and knowledge of the multivariate normal distribution. Specifically, the maximum likelihood estimators for the parameters of the multivariate normal population are often used in statistical inference settings. An approach to determining the maximum likelihood estimators is presented along with other important aspects of the multivariate normal distribution. Furthermore, both the likelihood ratio test and union intersection method of hypothesis testing are investigated in-depth. Discriminant analysis allows researchers to group data into pre-existing groups, and discriminant analysis is investigated here for two populations. Multiple discriminant rules are proved, including Fisher's linear discriminant function. In addition, the practical applications of discriminant analysis are demonstrated through the analysis of two data sets. In one application, thirteen variables are used to group homes based on median home cost. In another application, financial ratios are used to predict the bankruptcy status of banks.
© Katelin Lea Strand
Strand, Katelin Lea, "Multivariate Hypothesis Testing and Applications of Discriminant Analysis" (2014). MSU Graduate Theses. 1653.