Bayesian local influence analysis of general estimating equations with nonignorable missing data

2017 ◽  
Vol 105 ◽  
pp. 184-200 ◽  
Author(s):  
Yan-Qing Zhang ◽  
Nian-Sheng Tang
Keyword(s):  
Missing Data ◽  
Estimating Equations ◽  
Local Influence ◽  
Nonignorable Missing Data ◽  
Influence Analysis ◽  
Nonignorable Missing ◽  
Local Influence Analysis ◽  
Bayesian Local Influence

Semiparametric inverse propensity weighting for nonignorable missing data

Biometrika ◽  
2016 ◽  
Vol 103 (1) ◽  
pp. 175-187 ◽  
Author(s):  
Jun Shao ◽  
Lei Wang
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Generalized Method Of Moments ◽  
Estimating Equations ◽  
Real Data ◽  
Population Parameters ◽  
Finite Sample ◽  
External Data ◽  
Nonignorable Missing ◽  
Inverse Propensity Weighting

Abstract To estimate unknown population parameters based on data having nonignorable missing values with a semiparametric exponential tilting propensity, Kim & Yu (2011) assumed that the tilting parameter is known or can be estimated from external data, in order to avoid the identifiability issue. To remove this serious limitation on the methodology, we use an instrument, i.e., a covariate related to the study variable but unrelated to the missing data propensity, to construct some estimating equations. Because these estimating equations are semiparametric, we profile the nonparametric component using a kernel-type estimator and then estimate the tilting parameter based on the profiled estimating equations and the generalized method of moments. Once the tilting parameter is estimated, so is the propensity, and then other population parameters can be estimated using the inverse propensity weighting approach. Consistency and asymptotic normality of the proposed estimators are established. The finite-sample performance of the estimators is studied through simulation, and a real-data example is also presented.

Multiple Imputation Analysis for Nonignorable Missing Data

2021 ◽  
pp. 375-406
Author(s):  
Yulei He ◽  
Guangyu Zhang ◽  
Chiu-Hsieh Hsu
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Nonignorable Missing Data ◽  
Nonignorable Missing

scholarly journals Nonstandard conditionally specified models for nonignorable missing data

2020 ◽  
Vol 117 (32) ◽  
pp. 19045-19053
Author(s):  
Alexander M. Franks ◽  
Edoardo M. Airoldi ◽  
Donald B. Rubin
Keyword(s):  
Systems Biology ◽  
Missing Data ◽  
Joint Distribution ◽  
Exponential Family ◽  
Nonignorable Missing Data ◽  
Data Analyses ◽  
Missingness Mechanism ◽  
Conditionally Specified Models ◽  
Nonignorable Missing ◽  
Family Models

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey’s representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

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