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‘On the degrees of freedom of reduced-rank estimators in multivariate regression’

《多元回归中降秩估计量的自由度》证明缺陷说明

Biometrika (2015), 102, pp. 457–77 The argument given in the paper that Stein’s formula can be applied to the reduced-rank regression estimator is unfortunately incomplete. A full proof may be found in Hansen (2017). We thank Niels Richard Hansen for pointing out a deficiency in the proof in the paper.

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Large-sample theory for parametric multiple imputation procedures

参数化多重插补程序的大样本理论

We consider the asymptotic behaviour of various parametric multiple imputation procedures which include but are not restricted to the ‘proper’ imputation procedures proposed by Rubin (1978). The asymptotic variance structure of the resulting estimators is provided. This result is used to compare the relative efficiencies of different imputation procedures. It also provides a basis to understand the behaviour of two Monte Carlo iterative estimators, stochastic EM (Celeux &…

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A note on covariate measurement error in nonlinear mixed effects models

非线性混合效应模型中协变量测量误差的影响

Little is known about the effects of measurement error in intra-individual covariates on inference for the nonlinear mixed effects model. We investigate this issue for a controlled variable measurement error model, and find that a major consequence may be substantial bias in estimates of parameters characterising intra-individual variation. Estimation of population parameters may also be affected. The dramatic effect of measurement error on estimation of intra-individual…

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