rsem: Robust Structural Equation Modeling with Missing Data

rsem is a robust procedure for estimating structural equation models with missing data, developed by Ke-Hai Yuan and Zhiyong Zhang. The method uses Huber weights to estimate the means and covariance matrix of multiple variables before model estimation. It is hosted on the paperswithcode platform.

Use Cases

• Estimating structural equation models based on robust covariance matrices.
• Handling missing data in multivariate analysis using the described Huber weighting procedure.
• Comparing the performance of robust estimation methods against traditional SEM techniques.

Strengths

• Implements a specific robust estimation procedure using Huber weights.
• Addresses the common challenge of missing data in statistical modeling.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Ke-Hai Yuan and Zhiyong Zhang

Collection Method

Likely a software implementation of a statistical method.