# Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis

arXiv: https://arxiv.org/abs/2002.09358 · HTML: https://mlsanity.com/paper/2002.09358/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 11 of 11)

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- [ICTSurF: Implicit Continuous-Time Survival Functions with Neural Networks](https://mlsanity.com/paper/2312.05818/) (2023)
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- [Neural Network-Based Piecewise Survival Models](https://mlsanity.com/paper/2403.18664/) (2024)
- [Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data](https://mlsanity.com/paper/2503.09097/) (2025)
- [Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks](https://mlsanity.com/paper/2507.07804/) (2025)
- [WTNN: Weibull-Tailored Neural Networks for survival analysis](https://mlsanity.com/paper/2512.09163/) (2025)
- [Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis](https://mlsanity.com/paper/2605.04063/) (2026)
- [A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature](https://mlsanity.com/paper/2605.16208/) (2026)

## Abstract (arXiv)

In this paper, we consider survival analysis with right-censored data which is a common situation in predictive maintenance and health field. We propose a model based on the estimation of two-parameter Weibull distribution conditionally to the features. To achieve this result, we describe a neural network architecture and the associated loss functions that takes into account the right-censored data. We extend the approach to a finite mixture of two-parameter Weibull distributions. We first validate that our model is able to precisely estimate the right parameters of the conditional Weibull distribution on synthetic datasets. In numerical experiments on two real-word datasets (METABRIC and SEER), our model outperforms the state-of-the-art methods. We also demonstrate that our approach can consider any survival time horizon.