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How to Derive Bias and Mean Square Error for an Estimator?

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.

http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6530724

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23 February 2016 - 1:43pm
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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: Nov. 22, 2019.
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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