Anderson, Brian; Gevers, Michel
When one random variable is estimated from another measured random variable through a nonlinear mapping constituting the estimator, then any independent additive noise present in the measured variable creates a bias error in the estimated variable. This occurs even if the added noise has zero mean and symmetric density. This bias error can be computed approximately using the second derivative of the mapping when this mapping is available analytically, and hence a bias-corrected estimate can be...[Show more]
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