Copy to clipboardCopy Ng, Edmond Siu-Woon; (2011) MSE < Variance? A pitfall in calculating the mean square error. Model Assisted Statistics and Applications, 6 (4). pp. 369-371. DOI: https://doi.org/10.3233/mas-2011-0195
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Copy to clipboardCopyhttps://doi.org/10.3233/mas-2011-0195
When calculating the mean square error (MSE), it is possible to encounter a situation where the variance of a parameter of interest is larger than its mean square error. In theory, this is impossible because MSE is the sum of variance and bias squared; even when bias is zero, the MSE should be equal to, and not less than, the variance. This short note explains why this is indeed an error with a mathematical proof , demonstrates how this could happen using a small simulation study, and shows how to avoid making such an error in the derivation of the MSE. © 2011 IOS Press and the authors. All rights reserved.
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