Abstract:

Analyzing the performance of estimators is a typical task in signal processing. Two fundamental performance measures in the aspect of accuracy are bias and mean square error (MSE). In this presentation, we revisit a simple technique to produce the bias and MSE of an estimator that minimizes or maximizes an unconstrained differentiable cost function over a continuous space of the parameter vector under the small error conditions. This presentation is a companion work of: H. C. So, Y. T. Chan, K. C. Ho and Y. Chen, "Simple formulas for bias and mean square error computation," IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 162-165, July 2013.

## compute_bias_mse.pdf

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[1] ,
"How to Derive Bias and Mean Square Error for an Estimator?",
IEEE SigPort,
2015. [Online]. Available: http://sigport.org/194. Accessed: Jul. 21, 2019.

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title = {How to Derive Bias and Mean Square Error for an Estimator?},

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.
(2015).
How to Derive Bias and Mean Square Error for an Estimator?.
IEEE SigPort.
http://sigport.org/194

,
2015.
How to Derive Bias and Mean Square Error for an Estimator?.
Available at:
http://sigport.org/194.

.
(2015).
"How to Derive Bias and Mean Square Error for an Estimator?."
Web.

1. .
How to Derive Bias and Mean Square Error for an Estimator? [Internet].
IEEE SigPort; 2015.
Available from :
http://sigport.org/194