Practical Advice on How to Impute Continuous Data When the Ultimate Interest Centers on Dichotomized Outcomes Through Pre-Specified Thresholds

DOI10.1080/03610910701418424zbMath1126.62020OpenAlexW2054331551MaRDI QIDQ5436403

Hakan Demirtas

Publication date: 16 January 2008

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910701418424

zbMATH Keywords

multivariate normalityskewnessmultiple imputationlog-linear modelsmultimodality

Mathematics Subject Classification ID

Parametric inference (62F99) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (6)

An ECM Estimation Approach for Analyzing Multivariate Skew-Normal Data with Dropout ⋮ A nonnormal look at polychoric correlations: modeling the change in correlations before and after discretization ⋮ A distance-based rounding strategy for post-imputation ordinal data ⋮ Imputing continuous data under some non‐Gaussian distributions ⋮ An Appraisal of Methods for the Analysis of Longitudinal Ordinal Response Data with Random Dropout Using a Nonhomogeneous Markov Model ⋮ Multiple Imputation for Longitudinal Data Under a Bayesian Multilevel Model

Cites Work

• Unnamed Item
• Multiple Imputation After 18+ Years
• The Calculation of Posterior Distributions by Data Augmentation
• Inference and missing data
• Simulation driven inferences for multiply imputed longitudinal datasets*

This page was built for publication: Practical Advice on How to Impute Continuous Data When the Ultimate Interest Centers on Dichotomized Outcomes Through Pre-Specified Thresholds