|Title||Bayesian Sensitivity Analysis of Statistical Models with Missing Data.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Zhu, Hongtu, Joseph G. Ibrahim, and Niansheng Tang|
|Date Published||2014 Apr|
Methods for handling missing data depend strongly on the mechanism that generated the missing values, such as missing completely at random (MCAR) or missing at random (MAR), as well as other distributional and modeling assumptions at various stages. It is well known that the resulting estimates and tests may be sensitive to these assumptions as well as to outlying observations. In this paper, we introduce various perturbations to modeling assumptions and individual observations, and then develop a formal sensitivity analysis to assess these perturbations in the Bayesian analysis of statistical models with missing data. We develop a geometric framework, called the Bayesian perturbation manifold, to characterize the intrinsic structure of these perturbations. We propose several intrinsic influence measures to perform sensitivity analysis and quantify the effect of various perturbations to statistical models. We use the proposed sensitivity analysis procedure to systematically investigate the tenability of the non-ignorable missing at random (NMAR) assumption. Simulation studies are conducted to evaluate our methods, and a dataset is analyzed to illustrate the use of our diagnostic measures.
|Alternate Journal||Stat Sin|
|Original Publication||Bayesian sensitivity analysis of statistical models with missing data.|
|PubMed Central ID||PMC3991016|
|Grant List||R01 MH086633 / MH / NIMH NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
T32 CA106209 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P50 CA106991 / CA / NCI NIH HHS / United States
R01 CA074015 / CA / NCI NIH HHS / United States
Bayesian Sensitivity Analysis of Statistical Models with Missing Data.