All the Workings for GSOC-2019
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Updated
Sep 25, 2024 - HTML
All the Workings for GSOC-2019
Flexible parametric accelerated failure time models in Stata
This repository explores the integration of Machine Learning with the Weibull Distribution to improve the accuracy of Remaining Useful Life (RUL) estimations. By treating the Weibull scale parameter ( λ ) as a dynamic target for regression, these projects bridge the gap between statistical reliability analysis and modern data-driven maintenance.
A comprehensive Python survival analysis workflow using statsmodels. Covers Kaplan-Meier estimation, log-rank tests, Cox proportional hazards regression, and alternative approaches like Nelson-Aalen and Accelerated Failure Time models with practical code examples.
A hands-on guide to model selection, emphasizing high-dimensional problems, Bayesian model selection and averaging, and L0 criteria
Reference Survival Regression Modelling using Bayesian inference and a Bayesian workflow, specifically using the `pymc` & `arviz` ecosystem.
A survival analysis project based on the heart failure clinical records dataset
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