Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography
Abstract
Many estimators in signal processing problems are defined implicitly as the maximum of some objective function. Examples of implicitly defined estimators include maximum likelihood, penalized likelihood, maximum a posteriori, and nonlinear least squares estimation. For such estimators, exact analytical expressions for the mean and variance are usually unavailable. Therefore, investigators usually resort to numerical simulations to examine the...
Paper Details
Title
Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): applications to tomography
Published Date
Mar 1, 1996
Volume
5
Issue
3
Pages
493 - 506
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