scholarly journals On Testing the Missing at Random Assumption

2006 ◽  
pp. 671-678 ◽  
Author(s):  
Manfred Jaeger
Keyword(s):  
Missing At Random

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

scholarly journals Joint Longitudinal Models for Dealing With Missing at Random Data in Trial-Based Economic Evaluations

2021 ◽  
Author(s):  
Andrea Gabrio ◽  
Rachael Hunter ◽  
Alexina J. Mason ◽  
Gianluca Baio
Keyword(s):  
Missing At Random ◽  
Economic Evaluations ◽  
Longitudinal Models ◽  
Random Data

scholarly journals Efficient estimation from right-censored data when failure indicators are missing at random

1998 ◽  
Vol 26 (1) ◽  
pp. 164-182 ◽  
Author(s):  
Mark J. van der Laan ◽  
Ian W. McKeague
Keyword(s):  
Censored Data ◽  
Missing At Random ◽  
Efficient Estimation ◽  
Right Censored Data

Robustness to Parametric Assumptions in Missing Data Models

2011 ◽  
Vol 101 (3) ◽  
pp. 538-543 ◽  
Author(s):  
Bryan S Graham ◽  
Keisuke Hirano
Keyword(s):  
Empirical Bayes ◽  
Missing At Random ◽  
Data Driven ◽  
Robust Estimator ◽  
Sampling Variance ◽  
Bayes Estimators ◽  
Cell Means ◽  
Double Robust ◽  
Empirical Bayes Estimators ◽  
Empirical Bayes Estimator

We consider estimation of population averages when data are missing at random. If some cells contain few observations, there can be substantial gains from imposing parametric restrictions on the cell means, but there is also a danger of misspecification. We develop a simple empirical Bayes estimator, which combines parametric and unadjusted estimates of cell means in a data-driven way. We also consider ways to use knowledge of the form of the propensity score to increase robustness. We develop an empirical Bayes extension of a double robust estimator. In a small simulation study, the empirical Bayes estimators perform well. They are similar to fully nonparametric methods and robust to misspecification when cells are moderate to large in size, and when cells are small they maintain the benefits of parametric methods and can have lower sampling variance.

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