Date of Award

2017

Degree Type

Thesis

Degree Name

M.Sc.

Department

Mathematics and Statistics

First Advisor

Nkurunziza, Severien

Rights

CC BY-NC ND 4.0

Abstract

In this thesis, we consider an estimation problem concerning the matrix of correlation coe cients in context of high dimensional data settings. In particular, we generalise four main theorems in Li and Rosalsky [Li, D. and Rosalsky, A. (2006). Some strong limit theorems for the largest entries of sample correlation matrices, The Annals of Applied Probability, 16, 1, 423{447]. In addition, by using Erdos-Baum- Katz type Theorems, we also simplify remarkably the proofs of some results of Li and Rosalsky (2006). Further, we generalize a theorem which is useful in deriving the existence of the pth moment as well as in studying the convergence rates in Law of Large Numbers.

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