Parametric bootstrap for MSE estimation Sample Clauses

Parametric bootstrap for MSE estimation d The MSE of the EB estimator δˆEB with respect to the model is given by d d MSE(δˆEB) = E h(δˆEB − δd)2i, (2.13) d Note that here the target parameter δd is a random variable, so the usual decomposition of the MSE in terms of squared bias and variance of δˆEB does not hold. However, (2.13) can be decomposed as d d d d d MSE(δˆEB) = hE(δˆEB − δ )i2 + V (δˆEB − δ ). (2.14) Thus, the MSE is equal to the sum of the squared model bias and the variance of the prediction error.
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