# Statistical Jump Model for Mixed-Type Data with Missing Data Imputation

arXiv: https://arxiv.org/abs/2409.01208 · HTML: https://mlsanity.com/paper/2409.01208/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 60 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [k-POD: A Method for k-Means Clustering of Missing Data](https://mlsanity.com/paper/1411.7013/) (2014)
- [Tuning parameter selection in high dimensional penalized likelihood](https://mlsanity.com/paper/1605.03321/) (2016)
- [Fitting Jump Models](https://mlsanity.com/paper/1711.09220/) (2017)

## Built on by (most cited first, 2 of 2)

- [Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring](https://mlsanity.com/paper/2411.09726/) (2024)
- [Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering](https://mlsanity.com/paper/2606.13146/) (2026)

## Abstract (arXiv)

In this paper, we address the challenge of clustering mixed-type data with temporal evolution by introducing the statistical jump model for mixed-type data. This novel framework incorporates regime persistence, enhancing interpretability and reducing the frequency of state switches, and efficiently handles missing data. The model is easily interpretable through its state-conditional means and modes, making it accessible to practitioners and policymakers. We validate our approach through extensive simulation studies and an empirical application to air quality data, demonstrating its superiority in inferring persistent air quality regimes compared to the traditional air quality index. Our contributions include a robust method for mixed-type temporal clustering, effective missing data management, and practical insights for environmental monitoring.