A Comparison of Minimum Distance and Maximum Likelihood Techniques for Proportion Estimation.
Abstract
The estimation of mixing proportions in the mixture density is often encountered in agricultural remote sensing problems in which case the mixing proportions usually represent crop proportions. In these remote sensing applications, component densities have typically been assumed to be normally distributed, and parameter estimation has been accomplished using maximum likelihood (ML) techniques. In this paper we examine minimum distance (MD) estimation as an alternative to ML where, in this investigation, both procedures are based upon normal components. Results indicate that ML techniques are superior to MD when component distributions actually are normal, while MD estimation provides better estimates than ML under symmetric departures from normality. When component distributions are not symmetric, however, it is seen that neither of these normal based techniques provides satisfactory results.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1982
- Accession Number
- ADA125052
Entities
People
- H. L. Gray
- Hildegard Lindsey
- Wayne A. Woodward
- William R. Schucany
Organizations
- Southern Methodist University