Applied Multiple Imputation | Dr. Kristian Kleinke

Applied Multiple Imputation

Jan 28, 2022
more information about the book Springer R code (Chapters 5 and 6) R package countimp R package ImputeRobust
(c) Springer

Publications

Multiple imputation of incomplete panel data based on a piecewise growth curve model: An evaluation and application to juvenile delinquency data
Multiple imputation of longitudinal data: A comparison of robust imputation methods regarding sample size requirements, with an application to corporal punishment data
How to and how not to impute incomplete count data
Quantile regression based multiple imputation of missing data - An evaluation and application to corporal punishment data
Estimation of partially observed non-linear terms in a multilevel model - An evaluation of the robustness of ad hoc and state-of-the-art missing data methods
Proper multiple imputation of clustered or panel data
The development of delinquency during adolescence: a comparison of missing data techniques revisited
Applied Multiple Imputation. Advantages, Pitfalls, New Developments and Applications in R
Chapters Introduction and Basic Concepts pp 1-22 Missing Data Mechanism and Ignorability pp 23-52 Missing Data Methods pp 53-83 Multiple Imputation: Theory pp 85-131 Multiple Imputation: Application pp.
countimp version 2 – A multiple imputation package for incomplete count data
Kleinke, K., & Reinecke, J. (2019). countimp version 2 – A multiple imputation package for incomplete count data (Technical Report). Siegen, Germany: University of Siegen, Department of Education Studies and Psychology.
Multiple Imputation by Predictive Mean Matching When Sample Size Is Small
Multiple Imputation Under Violated Distributional Assumptions: A Systematic Evaluation of the Assumed Robustness of Predictive Mean Matching
Multiple imputation of overdispersed multilevel count data.
Multiple imputation of multilevel count data.
Multiple Imputation of Zero-Inflated and Overdispersed Multilevel Count Data
Multiple imputation of incomplete zero‐inflated count data
countimp 1.0 – A Multiple Imputation Package for Incomplete Count Data
Efficient ways to impute incomplete panel data
Multiple imputation of incomplete ordinary and overdispersed count data

Events

Robust multiple imputation based on quantile forests
Feb 9, 2023 3:00 PM Bielefeld, Germany
Multiple imputation of incomplete panel data based on a piecewise growth curve model
Feb 9, 2023 11:00 AM Bielefeld, Germany