Global ETD Search

1	Interlacing zeros of linear combinations of classical orthogonal polynomials Mbuyi Cimwanga, Norbert 04 June 2010 (has links) Please read the abstract in the front of this document. / Thesis (PhD)--University of Pretoria, 2009. / Mathematics and Applied Mathematics / unrestricted Linear combinations Classical orthogonal polynomials UCTD
2	The Joint Distribution of Two Linear Combinations of Random Variables Uniformly Distributed on a Simplex Lim, Siok 09 1900 (has links) <p> This thesis deals with linear combinations of a set of random variables uniformly distributed on a simplex. The exact joint distribution of two general linear combinations with real constant coefficients is considered and the results found in the form of the joint probability density function. Application of the result is also illustrated. </p> / Thesis / Master of Science (MSc) linear combinations simplex random variables joint distribution
3	STATISTICAL METHODS IN MICROARRAY DATA ANALYSIS Huang, Liping 01 January 2009 (has links) This dissertation includes three topics. First topic: Regularized estimation in the AFT model with high dimensional covariates. Second topic: A novel application of quantile regression for identification of biomarkers exemplified by equine cartilage microarray data. Third topic: Normalization and analysis of cDNA microarray using linear contrasts. Applied Statistics
4	Optimal Linear Combinations of Portfolios Subject to Estimation Risk Jonsson, Robin January 2015 (has links) The combination of two or more portfolio rules is theoretically convex in return-risk space, which provides for a new class of portfolio rules that gives purpose to the Mean-Variance framework out-of-sample. The author investigates the performance loss from estimation risk between the unconstrained Mean-Variance portfolio and the out-of-sample Global Minimum Variance portfolio. A new two-fund rule is developed in a specific class of combined rules, between the equally weighted portfolio and a mean-variance portfolio with the covariance matrix being estimated by linear shrinkage. The study shows that this rule performs well out-of-sample when covariance estimation error and bias are balanced. The rule is performing at least as good as its peer group in this class of combined rules. Markowitz Portfolio Optimization Diversification Convex Optimums Linear Combinations Estimation Risk Parameter Uncertainty Global Minimum Variance Mean-Variance Analysis Naive Diversification Modern Portfolio Theory Allocation Shrinkage Covariance Estimation

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