Pseudo Maximum Likelihood Estimation: Theory and Applications.
Abstract
Pseudo maximum likelihood estimation easily extends to k parameter models, and is of interest in problems in which the likelihood surface is ill-behaved in higher dimensions but well-behaved in lower dimensions. Several signal plus noise or convolution models are examined which exhibit such behavior and satisfy the regularity conditions of the asymptotic theory. For specific models, a numerical comparison of asymptotic variances suggests that a psuedo maximum likelihood estimate of the signal parameter is uniformly more efficient than estimators that have been advanced by previous authors. A number of other potential applications are noted.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1978
- Accession Number
- ADA060387
Entities
People
- Francisco J. Samaniego
- Gail G. Hannon
Organizations
- University of California, Davis