Quantum mechanics

Algorithm

Norm (philosophy)

Delta method

Statistics

Consistent estimator

Applied mathematics

Poisson distribution

Accounting

Computer science

Cramér–Rao bound

Business

Uniform norm

Physics

Variance (accounting)

Upper and lower bounds

Mathematics

Stein's unbiased risk estimate

Estimator

Political science

Econometrics

Mathematical analysis

Bias of an estimator

Minimum-variance unbiased estimator

Mean squared error

Invariant estimator

Sigma

IEEE Transactions on Signal Processing

We introduce a plane, which we call the delta-sigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a uniform Cramer-Rao (CR) bound on estimator variance, a delta-sigma tradeoff curve is specified that defines an "unachievable region" of the delta-sigma plane for a specified statistical model. In order to place an estimator on this plane for comparison with the delta-sigma tradeoff curve, the estimator variance, bias gradient, and bias gradient norm must be evaluated. We present a simple and accurate method for experimentally determining the bias gradient norm based on applying a bootstrap estimator to a sample mean constructed from the gradient of the log-likelihood. We demonstrate the methods developed in this paper for linear Gaussian and nonlinear Poisson inverse problems.

Exploring estimator bias-variance tradeoffs using the uniform CR bound