Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information

Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: invariance and finite-sample distributional theory

We study the distribution of Durbin-Wu-Hausman (DWH) and Revankar-Hartley (RH) tests for exogeneity from a finite-sample viewpoint, under the null and alternative hypotheses. We consider linear structural models with possibly non-Gaussian errors, where structural parameters may not be identified and where reduced forms can be incompletely specified (or nonparametric). On level control, we characterize the null distributions of all the test statistics. Through conditioning and invariance arguments, we show that these distributions do not involve nuisance parameters. In particular, this applies …

An information criterion for model selection with missing data via complete-data divergence

We derive an information criterion to select a parametric model of complete-data distribution when only incomplete or partially observed data is available. Compared with AIC, our new criterion has an additional penalty term for missing data, which is expressed by the Fisher information matrices of complete data and incomplete data. We prove that our criterion is an asymptotically unbiased estimator of complete-data divergence, namely, the expected Kullback-Leibler divergence between the true distribution and the estimated distribution for complete data, whereas AIC is that for the incomplete d…

单变量正态小样本下不完全数据估计的偏倚和效率

The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples

分析缺失数据的广泛使用的方法在小样本中可能有偏差。为了理解这些偏差，我们详细评估了一个小的单变量正态样本的情况，随机缺失的值，使用数据最大似然（ML）或多重插补（MI）进行分析。我们评估两种类型的MI：通常的贝叶斯方法，我们称之为后验抽取（PD）插补，和一个很少使用的替代方法，我们称之为ML插补，其中值是有条件地插补ML估计。我们发现，数据挖掘ML是更有效的，具有较低的均方误差比任何类型的MI。在两种类型的MI之间，ML插补比PD插补更有效，ML插补在小样本中偏倚的可能性也较小。通过改变先验知识可以改善PD插补的偏倚和效率。

结构方程建模中的设定错误：潜变量、因果形成型构念或合成变量的选择

Misspecifications in structural equation modeling: The choice of latent variables, causal-formative constructs or composites

该研究针对结构方程建模中的构念设定错误问题，通过蒙特卡洛模拟使用统一估计量考察三类构念真实与假定类型的所有组合，证明设定错误会导致路径系数估计偏差，且现有拟合准则无法识别该类错误，强调审慎设定构念的重要性。

Two-sample instrumental variable analyses using heterogeneous samples

Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not measured in the same sample, Angrist and Krueger (1992) suggested to use two-sample instrumental variable (TSIV) estimators that use sample moments from an instrument-exposure sample and an instrument-outcome sample. However, this method is biased if the two samples are from heterogeneous populations so that the distributions of the instruments are different. In linear structural equation models, we derive a new class of…