Search for dissertations about: "Covariance Matrix Estimation"
Showing result 1 - 5 of 66 swedish dissertations containing the words Covariance Matrix Estimation.
-
1. Exploiting Prior Information in Parametric Estimation Problems for Multi-Channel Signal Processing Applications
Abstract : This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. READ MORE
-
2. Modeling the covariance matrix of financial asset returns
Abstract : The covariance matrix of asset returns, which describes the fluctuation of asset prices, plays a crucial role in understanding and predicting financial markets and economic systems. In recent years, the concept of realized covariance measures has become a popular way to accurately estimate return covariance matrices using high-frequency data. READ MORE
-
3. Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression
Abstract : This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. READ MORE
-
4. Estimation Problems in Array Signal Processing, System Identification, and Radar Imagery
Abstract : This thesis is concerned with parameter estimation, signal processing, and applications. In the first part, imaging using radar is considered. More specifically, two methods are presented for estimation and removal of ground-surface reflections in ground penetrating radar which otherwise hinder reliable detection of shallowly buried landmines. READ MORE
-
5. Contributions to Estimation and Testing Block Covariance Structures in Multivariate Normal Models
Abstract : This thesis concerns inference problems in balanced random effects models with a so-called block circular Toeplitz covariance structure. This class of covariance structures describes the dependency of some specific multivariate two-level data when both compound symmetry and circular symmetry appear simultaneously. READ MORE