This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

This article proposes a data-driven method to identify parsimony in the covariance matrix of longitudinal data and to exploit any such parsimony to produce a statistically efficient estimator of the ...

Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, covariance forecasting models are optimized with unique prediction-based ...

Methods of Rubin (1983) for robust estimation of a mean and covariance matrix and associated parameters are extended to analyse data with missing values. The methods are maximum likelihood (ML) for ...

The following example demonstrates how you can use the ACECLUS procedure to obtain approximate estimates of the pooled within-cluster covariance matrix and to compute canonical variables for ...

X ij = [x ij1, ... , x ijp]' The Generalized Estimating Equation of Liang and Zeger (1986) for estimating the p ×1 vector of regression parameters is an extension of the independence estimating ...