# Estimation of Conditional Mixture Weibull Distribution with Right Censored Data Using Neural Network for Time-to-Event Analysis

@article{Bennis2020EstimationOC, title={Estimation of Conditional Mixture Weibull Distribution with Right Censored Data Using Neural Network for Time-to-Event Analysis}, author={A.-C. Bennis and Sandrine Mouysset and Mathieu Serrurier}, journal={Advances in Knowledge Discovery and Data Mining}, year={2020}, volume={12084}, pages={687 - 698} }

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…

## 3 Citations

Inferring latent heterogeneity using many feature variables supervised by survival outcome

- Computer ScienceStatistics in medicine
- 2021

This work proposes a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution, and shows that the adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size.

Learning Interpretable Mixture of Weibull Distributions - Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths

- EconomicsData
- 2021

An algorithm for learning local Weibull models, whose operating regions are represented by fuzzy rules, that can estimate efficiently the mortality rate of countries due to their economic situation, urbanization, and the state of the health sector is presented.

## References

SHOWING 1-10 OF 16 REFERENCES

Bayesian Estimation of a Weibull Distribution in a Highly Censored and Small Sample Setting

- Mathematics
- 1996

We propose and investigate through Monte Carlo simulations two methods for Bayesian inference for the shape and the scale parameters of a Weibull distribution in a small and highly censored sample…

Parameter estimation for Weibull distribution with right censored data using EM algorithm

- Mathematics
- 2017

The estimation process is supported by a number of statistical techniques, methods and procedures for analyzing the data on the variable of interest that may be the time that elapses from the…

A neural network model for survival data.

- Computer ScienceStatistics in medicine
- 1995

This paper presents an approach to modelling censored survival data using the input-output relationship associated with a simple feed-forward neural network as the basis for a non-linear proportional hazards model.

DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks

- Computer ScienceAAAI
- 2018

A very different approach to survival analysis, DeepHit, that uses a deep neural network to learn the distribution of survival times directly and achieves large and statistically significant performance improvements over previous state-of-the-art methods.

ESTIMATIONS OF THE PARAMETERS OF THE WEIBULL DISTRIBUTION WITH PROGRESSIVELY CENSORED DATA

- Mathematics
- 2002

We obtained estimation results concerning a progressively type-II censored sample from a two-parameter Weibull distribution. The maximum likelihood method is used to derive the point estimators of…

Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors

- Computer ScienceNIPS
- 2011

A local regression method for learning patient-specific survival time distribution based on patient attributes such as blood tests and clinical assessments is proposed and gives survival time predictions that are much more accurate than popular survival analysis models such as the Cox and Aalen regression models.

Nonparametric Estimation from Incomplete Observations

- Mathematics
- 1958

Abstract In lifetesting, medical follow-up, and other fields the observation of the time of occurrence of the event of interest (called a death) may be prevented for some of the items of the sample…

Combining Deep Learning and Survival Analysis for Asset Health Management

- Computer Science
- 2020

The proposed method to integrate feature extraction and prediction as a single optimization task by stacking a three-layer model as a deep learning structure resulted in the “individualized” failure probability representation for assessing the health condition of each individual asset.

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

- Computer ScienceArXiv
- 2017

A deep learning method is proposed that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning, which outperforms other common methods for survival analysis.