# A nested expectation-maximization algorithm for latent class models with covariates

## TL;DR

This paper introduces a nested EM algorithm for latent class models with covariates that ensures monotonic log-likelihood improvement and faster convergence compared to existing methods.

## Contribution

It presents a novel nested EM routine that guarantees monotone log-likelihood sequences and enhances convergence speed for latent class models with covariates.

## Key findings

- Guarantees monotone increase in log-likelihood
- Achieves improved convergence rates
- Applicable to latent class models with covariates

## Abstract

We develop a nested EM routine for latent class models with covariates which allows maximization of the full-model log-likelihood and, differently from current methods, guarantees monotone log-likelihood sequences along with improved convergence rates.