Analyzing evidence-based falls prevention data with significant missing information using variable selection after multiple imputation

DOI10.1080/02664763.2021.1985090OpenAlexW3205678006MaRDI QIDQ6157149

Changwei Li, Yujia Cheng, Unnamed Author, Ye Shen, Yang Li

Publication date: 19 June 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9930815

zbMATH Keywords

multiple imputationvariable selectionstepwise regressiondata simulationRubin's rulesgroup Lasso penaltyfall preventionfalls efficacy

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Statistics for high-dimensional data. Methods, theory and applications.
• On cross-validated Lasso in high dimensions
• Fully conditional specification in multivariate imputation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables
• Methods and Criteria for Model Selection
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