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Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data

Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

In this chapter we introduce Hybrid Monte-Carlo (HMC) as an efficient method to sample from complex posterior distributions of many correlated parameters from a semi-parametric missing data model. The HMC enables a distribution-free likelihood-based approach to multiple imputation of missing values. We describe the modeling approach for modeling missing values that does not require assuming any specific distributional forms. We then describe the use of the HMC sampler to obtain inferences and generate multiple imputations under the model, and touch upon various implementation issues, such as choosing starting values, determining burn-in period, monitoring convergence , deciding stopping times. An R program is provided for analyzing missing data from a Bone Fracture study.

Keywords

Multiple Imputation Dirichlet Process Imputation Model Impute Dataset Full Conditional Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.The University of Illinois at ChicagoChicagoUSA

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