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Basic Multivariate Distributions
Sýkorová, Sabina ; Kulich, Michal (advisor) ; Hurt, Jan (referee)
The thesis deals with the basic discrete and continuous multivariate distributions, which play an important role in statistical analyses of models in applied fields. It focuses mainly on the derivation of these distributions using various techniques by which univariate distributions are generalized to higher dimensions. At the beginning of the thesis the multivariate normal distribution is defined, than it deals with distributions that are derived by direct generalization of univariate distributions. These are multivariate log-normal, multivariate Student's, multivariate Pareto, Dirichlet, and multinomial distributions. Furthermore it describes a common components method by which a multivariate Poisson distribution and a multivariate gamma distribution are derived. In the last chapter we introduce a multivariate exponential distribution derived by a stochastic generalization technique.
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Selected problems and methods in multivariate data analysis
Goduľová, Lenka ; Zichová, Jitka (advisor) ; Hurt, Jan (referee)
Title: Selected problems and methods in multivariate data analysis Author: Lenka Goduľová Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Jitka Zichová, Dr. Abstract: The bachelor thesis deals with processing multidimensional data. The task was to apply selected methods on financial data. The thesis is composed of the theoretical section and the analysis of a particular database. The first four chapters deal with basic relations and definitions concerning random vector and variable, multidimensional data and the independence test in a contingency table. The following section is devoted to defining the particular methods selected: cluster analysis and discriminant analysis. In the practical section these methods are applied to a database of clients of a German bank. Keywords: random vector, multivariate distribution, multivariate random variable, contingency table, cluster analysis, discriminant analysis.
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Selected problems and methods in multivariate data analysis
Goduľová, Lenka ; Zichová, Jitka (advisor) ; Hurt, Jan (referee)
Title: Selected problems and methods in multivariate data analysis Author: Lenka Goduľová Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Jitka Zichová, Dr. Abstract: The bachelor thesis deals with processing multidimensional data. The task was to apply selected methods on financial data. The thesis is composed of the theoretical section and the analysis of a particular database. The first four chapters deal with basic relations and definitions concerning random vector and variable, multidimensional data and the independence test in a contingency table. The following section is devoted to defining the particular methods selected: cluster analysis and discriminant analysis. In the practical section these methods are applied to a database of clients of a German bank. Keywords: random vector, multivariate distribution, multivariate random variable, contingency table, cluster analysis, discriminant analysis.
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Basic Multivariate Distributions
Sýkorová, Sabina ; Kulich, Michal (advisor) ; Hurt, Jan (referee)
The thesis deals with the basic discrete and continuous multivariate distributions, which play an important role in statistical analyses of models in applied fields. It focuses mainly on the derivation of these distributions using various techniques by which univariate distributions are generalized to higher dimensions. At the beginning of the thesis the multivariate normal distribution is defined, than it deals with distributions that are derived by direct generalization of univariate distributions. These are multivariate log-normal, multivariate Student's, multivariate Pareto, Dirichlet, and multinomial distributions. Furthermore it describes a common components method by which a multivariate Poisson distribution and a multivariate gamma distribution are derived. In the last chapter we introduce a multivariate exponential distribution derived by a stochastic generalization technique.
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