QA486 : Shrinkage Covariance Matrix Estimators in High-Dimensional Settings
Thesis > Central Library of Shahrood University > Mathematical Sciences > MSc > 2018
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Abstarct: In multivariate analysis, one of the most challenging cases of estimating a covariance matrix of a random variable is when the number of variables (dimension) is large. Also, in some multivariate discussions, it is necessary to estimate the function of a covariance matrix. Suppose p represents the dimension. In this thesis, we examine the covariance matrix estimator and some functions of it for the high dimension (p→∞). In this regard, we assume that the population of sampling has normal and non-normal distribution and introduce non-parametric shrinkage estimators of the covariance matrix in the high dimension and examine some of their properties. Also, using simulation examples, we evaluate the efficiency of the proposed shrinkage estimators for the covariance matrix in the high dimension.
Keywords:
#Covariance Matrix #Shrinkage Estimator #High Dimension #Consistent of Nonparametric Estimator. Link
Keeping place: Central Library of Shahrood University
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Keeping place: Central Library of Shahrood University
Visitor: