Conformal Statistical Inference: A Tutorial for Biostatistics and Machine Learning Applications
This tutorial introduces Conformal Statistical Inference — a distribution-free framework for constructing prediction sets with valid finite-sample coverage guarantees. It covers: Foundations: exchangeability, nonconformity scores, coverage probability guarantees Split Conformal Prediction (SCP): computationally efficient, data-splitting approach Conformal Quantile Regression (CQR): adaptive intervals handling heteroscedasticity Full Conformal Prediction: exhaustive leave-one-out calibration Jackknife+: conservative leave-one-out conformal with improved properties Resources 🎓 Tutorial Slides (Spanish): Quarto RevealJS slides 📘 Comprehensive Guide (English): Interactive notebook 💻 Reproducible code available on GitHub
2017
The Delta-Method and Influence Function in Medical Statistics: a Reproducible Tutorial.
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Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application .
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Ensemble Learning Targeted Maximum Likelihood Estimation, at London.
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