A Monte Carlo Comparison of Four Estimators of the Dispersion Matrix of a Bivariate Normal Population, Using Incomplete Data
Abstract
Consider a random vector (X(1), X(2)) distributed as a bivariate normal with mean vector zero, and dispersion matrix sigma = ((sigma sub ii)). Suppose the authors are given samples of sizes n(1) and n(2), respectively, from the marginals of X(1), X(2), and a sample of size n(3) from the bivariate population of (X(1), X(2)). Suppose the problem is to obtain a good estimator of sigma based on the above (incomplete) sample. In the paper, four estimators of sigma are compared using Monte Carlo methods, and it is found that a certain relatively simple estimator of sigma is the 'best' or close to the best in almost all situations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1972
- Accession Number
- AD0741802
Entities
People
- J. N. Srivastava
- M. K. Zaatar
Organizations
- Colorado State University