Quantile Regression-Based Multiple Imputation of Missing Values — An Evaluation and Application to Corporal Punishment Data
Quantile regression (QR) is a valuable tool for data analysis and multiple imputation (MI) of missing values – especially when standard parametric modelling assumptions are violated. Yet, Monte Carlo simulations that systematically evaluate QR-based MI in a variety of different practically relevant...
Main Authors: | Kristian Kleinke, Markus Fritsch, Mark Stemmler, Jost Reinecke, Friedrich Lösel |
---|---|
Format: | Article |
Language: | English |
Published: |
PsychOpen GOLD/ Leibniz Institute for Psychology
2021-09-01
|
Series: | Methodology |
Subjects: | |
Online Access: | https://meth.psychopen.eu/index.php/meth/article/view/2317 |
Similar Items
-
Compromised-Imputation and EWMA-Based Memory-Type Mean Estimators Using Quantile Regression
by: Mohammed Ahmed Alomair, et al.
Published: (2023-10-01) -
Clustering column-mean quantile median: a new methodology for imputing missing data
by: Nourhan Yehia, et al.
Published: (2022-12-01) -
Composite Quantile Regression for Varying Coefficient Models with Response Data Missing at Random
by: Shuanghua Luo, et al.
Published: (2019-08-01) -
A computational strategy for estimation of mean using optimal imputation in presence of missing observation
by: Subhash Kumar Yadav, et al.
Published: (2024-03-01) -
Methodological approaches for imputing missing data into monthly flows series
by: Michel Trarbach Bleidorn, et al.
Published: (2022-04-01)