# DPERC: Direct Parameter Estimation for Mixed Data

arXiv: https://arxiv.org/abs/2501.10540 · HTML: https://mlsanity.com/paper/2501.10540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning](https://mlsanity.com/paper/1912.06989/) (2019)
- [Multiple imputation for continuous variables using a Bayesian principal component analysis](https://mlsanity.com/paper/1401.5747/) (2014)
- [EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data](https://mlsanity.com/paper/2009.11360/) (2020)
- [Correlation visualization under missing values: a comparison between imputation and direct parameter estimation methods](https://mlsanity.com/paper/2305.06044/) (2023)
- [Improved Covariance Matrix Estimator using Shrinkage Transformation and Random Matrix Theory](https://mlsanity.com/paper/1912.03718/) (2019)

## Abstract (arXiv)

The covariance matrix is a foundation in numerous statistical and machine-learning applications such as Principle Component Analysis, Correlation Heatmap, etc. However, missing values within datasets present a formidable obstacle to accurately estimating this matrix. While imputation methods offer one avenue for addressing this challenge, they often entail a trade-off between computational efficiency and estimation accuracy. Consequently, attention has shifted towards direct parameter estimation, given its precision and reduced computational burden. In this paper, we propose Direct Parameter Estimation for Randomly Missing Data with Categorical Features (DPERC), an efficient approach for direct parameter estimation tailored to mixed data that contains missing values within continuous features. Our method is motivated by leveraging information from categorical features, which can significantly enhance covariance matrix estimation for continuous features. Our approach effectively harnesses the information embedded within mixed data structures. Through comprehensive evaluations of diverse datasets, we demonstrate the competitive performance of DPERC compared to various contemporary techniques. In addition, we also show by experiments that DPERC is a valuable tool for visualizing the correlation heatmap.