Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information
Journal of VLSI Signal Processing Systems
Statistical approaches in quantitative positron emissiontomography
Statistics and Computing
On the adaptive linear estimators, using biased Cramér-Rao bound
Signal Processing
Towards optimal sampling for flow size estimation
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
Foundations and Trends in Signal Processing
Minimum variance in biased estimation with singular fisher information matrix
IEEE Transactions on Signal Processing
Empirical evaluation of the limits on localization using signal strength
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Super-resolution reconstruction of MR image sequences with contrast modeling
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Optimal illumination patterns for fluorescence tomography
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
A lower bound on the Bayesian MSE based on the optimal bias function
IEEE Transactions on Information Theory
Noisy data and impulse response estimation
IEEE Transactions on Signal Processing
Optimality analysis of sensor-target localization geometries
Automatica (Journal of IFAC)
On the optimal performance of collaborative position location
IEEE Transactions on Wireless Communications
Enhancing RSSI-based tracking accuracy in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 35.75 |
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