Sparse Markov Model Fitting Results for Categorical Time Series

11.8 MB of simulation results and analysis supporting a method for fitting Sparse Markov Models to categorical time series. The dataset, authored by Tuhin Majumder and last updated in May 2026, includes PDF, ZIP, and TXT files detailing an approach using convex clustering and regularization. It contains extensive simulation studies under different set-ups and a real data analysis on disease sub-type modeling and classification.

Use Cases

• Benchmarking Sparse Markov Model (SMM) fitting algorithms based on the described convex clustering method
• Studying model selection consistency for large sample sizes as referenced in the theoretical results
• Classifying disease sub-types using the demonstrated real data analysis approach

Strengths

• Dataset size is 11.8 MB, indicating substantive supporting material
• Method is detailed with extensive simulation results under different set-ups
• Includes a real-world application for modeling and classifying disease sub-types

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

figshare

Collection Method

Likely contains results from simulation studies and a real data analysis, as described in the accompanying paper.

Freshness

Last updated 2026-05-26 17:53:50; freshness should be verified