A comparison of minimum distance and maximum likelihood techniques for proportion estimation

Woodward, W. A.; Schucany, W. R.; Lindsey, H.; Gray, H. L.

The estimation of mixing proportions P sub 1, P sub 2,...P sub m in the mixture density f(x) = the sum of the series P sub i F sub i(X) with i = 1 to M is often encountered in agricultural remote sensing problems in which case the p sub i's usually represent crop proportions. In these remote sensing applications, component densities f sub i(x) have typically been assumed to be normally distributed, and parameter estimation has been accomplished using maximum likelihood (ML) techniques. Minimum distance (MD) estimation is examined 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 ID

19830026124

Acquisition Source

Legacy CDMS

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Contractor Report (CR)

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Date Acquired

September 4, 2013

Publication Date

November 1, 1982

Subject Category

Report/Patent Number

Accession Number

83N34395

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Public

Work of the US Gov. Public Use Permitted.

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