Iterative generalized cross-validation for fusing heteroscedastic data of inverse ill-posed problems

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Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis (2012, methodology): “Now we will follow the bias correction technique of Xu (2009) (see also Xu and Shimada 2000; Xu et al. 2006) to construct an estimate of σ 2.”
Variance components in errors-in-variables models: estimability, stability and bias analysis (2014, methodology): “The proof can be completed by following the partial EIV formulation of Xu et al. (2012) and then using the same rationales as used to prove the inestimability in this section.”
Bias-corrected regularized solution to inverse ill-posed models (2012, methodology): “Actually, Xu et al. (2006b) and Xu (2009) extended the inverse ill-posed model (1) to combine different types of data with a number of unknown variance components.”