Simon Fraser University

Covariance Matrix

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...
JSTOR Daily

An Unbiased Estimator of the Covariance Matrix of the Mixed Regression Estimator

This article derives an unbiased estimator of the covariance matrix of the "mixed regression" estimator suggested by Theil and Goldberger for combining prior information with the sample information in ...
Nature

Graphical Models and Covariance Matrix Estimation

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
International Monetary Fund

High-Dimensional Covariance Matrix Estimation: Shrinkage Toward a Diagonal Target

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle ...
Risk

Finding the nearest covariance matrix: the foreign exchange market case

We consider the problem of finding a valid covariance matrix in the foreign exchange market given an initial nonpositively semidefinite (non-PSD) estimate of such a matrix. The common no-arbitrage ...
JSTOR Daily

Confirmatory Factor Analysis via Noniterative Estimation: A Fast, Inexpensive Method

Journal of Marketing Research, Vol. 19, No. 4, Special Issue on Causal Modeling (Nov., 1982), pp. 417-424 (8 pages) A new method of estimating the parameters of confirmatory and exploratory factor ...
Simon Fraser University

Covariance Matrix

The estimated covariance matrix of the parameter estimates is computed as the inverse Hessian matrix, and for unconstrained problems it should be positive definite. If the final parameter estimates ...