IPLSL and IPLSQ: Two types of imputation PLS algorithms for hierarchical latent variable model

DOI10.1080/03610926.2021.1955382OpenAlexW3191292484MaRDI QIDQ6106208

Hao Cheng

Publication date: 27 June 2023

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2021.1955382

zbMATH Keywords

missing datapartial least squareformative measurement modelhierarchical latent variable model

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• PLS path modeling
• Quantile composite-based path modeling
• Parametric fractional imputation for missing data analysis
• Handbook of partial least squares. Concepts, methods and applications.
• Regression Quantiles
• Weighted Semiparametric Estimation in Regression Analysis With Missing Covariate Data
• Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus
• Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models
• A General Framework for Quantile Estimation with Incomplete Data
• Assessment and Validation in Quantile Composite-Based Path Modeling
• Efficient Quantile Regression Analysis With Missing Observations
• A Generalization of Sampling Without Replacement From a Finite Universe