# Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models

arXiv: https://arxiv.org/abs/1903.09235 · HTML: https://mlsanity.com/paper/1903.09235/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Support Recovery of Sparse Signals from a Mixture of Linear Measurements](https://mlsanity.com/paper/2106.05951/) (2021)
- [Efficient Algorithms for Estimating the Parameters of Mixed Linear Regression Models](https://mlsanity.com/paper/2105.05953/) (2021)
- [Global Convergence of Online Identification for Mixed Linear Regression](https://mlsanity.com/paper/2311.18506/) (2023)
- [Predicting Antimicrobial Resistance in the Intensive Care Unit](https://mlsanity.com/paper/2111.03575/) (2021)

## Abstract (arXiv)

We consider em Mixed Linear Regression (MLR), where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a em Mixed Integer Programming (MIP) formulation for MLR subject to regularization constraints on the coefficient vectors. We establish that as the number of training samples grows large, the MIP solution converges to the true coefficient vectors in the absence of noise. Subject to slightly stronger assumptions, we also establish that the MIP identifies the clusters from which the training samples were generated. In the special case where training data come from a single cluster, we establish that the corresponding MIP yields a solution that converges to the true coefficient vector even when training data are perturbed by (martingale difference) noise. We provide a counterexample indicating that in the presence of noise, the MIP may fail to produce the true coefficient vectors for more than one clusters. We also provide numerical results testing the MIP solutions in synthetic examples with noise.