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Provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings.
Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives.
He describes ea
Handbook of Missing Data Methodology
Multiple Imputation of Missing Data in Practice
Applied Missing Data Analysis in the Health Sciences
Statistical Methods for Handling Incomplete Data
Statistical Analysis with Missing Data
Missing Data
Longitudinal Data Analysis
Analysis of Integrated Data
Applied Linear Regression for Longitudinal Data
Missing Data in Longitudinal Studies
Preventing and Treating Missing Data in Longitudinal Clinical Trials
Applying Quantitative Bias Analysis to Epidemiologic Data