Flexible parametric accelerated failure time models in Stata
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Jan 20, 2022 - Stata
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accelerated-failure-time
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All the Workings for GSOC-2019
python machine-learning r machine-learning-algorithms python3 xgboost survival-analysis gradient-boosting-machine accelerated-failure-time
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Sep 25, 2024 - HTML
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.
survival-analysis kaplan-meier regression-models statsmodels time-to-event survival-curves log-rank-test accelerated-failure-time cox-proportional-hazards hazard-ratio nelson-aalen cumulative-hazard proportional-hazards-assumption nonparametric-estimation
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Dec 17, 2025 - Jupyter Notebook
Reference Survival Regression Modelling using Bayesian inference and a Bayesian workflow, specifically using the `pymc` & `arviz` ecosystem.
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Nov 24, 2025 - Jupyter Notebook
A hands-on guide to model selection, emphasizing high-dimensional problems, Bayesian model selection and averaging, and L0 criteria
regression graphical-models bayesian-statistics generalized-linear-models generalized-additive-models bayesian-model-averaging accelerated-failure-time bayesian-model-selection l0-regularization
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Dec 5, 2025 - TeX
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