multiple-testing

Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control

The MultipleTesting package offers common algorithms for p-value adjustment and combination and more…

Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023

Statistical inference for Sharpe ratios: probabilistic Sharpe ratio, minimum track record length, and FDR/FWER corrections for screening many strategies

Statistical inference of recent positive selection using IBD segments

Repository for R and Python packages and reproduction codes in Weighted Conformalized Selection paper

This repository contains a collection of functions to evaluate investment strategies regarding multiple testing concerns.

A Shiny app for graphical multiplicity control

Fast exact grid search and backtesting of moving-average crossover strategies, with vectorized evaluation and multiple-testing-aware Sharpe diagnostics.

Sequential Hypothesis Testing with e-Values and p-Values

A FDR controlling procedure based on hidden Markov random field (Biometrics-15 paper)

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes

LLM-driven evolutionary alpha factor mining on S&P 500 stocks (CRSP, 2000-2025): six search arms (five Claude, one genetic programming) against random formulas and Alpha101, with an 18-niche archive, Holm-corrected holdout tests and prompt-to-factor lineage.

NumPy-only Independent Hypothesis Weighting for covariate-weighted multiple testing, with transparent validation and performance benchmarks.

Fixed Sequence Multiple Testing Procedures

Methods in my PhD thesis

Monte Carlo audit of multiple-testing procedures under block dependence

Backtest overfitting audit for factor research: probability of backtest overfitting (PBO), deflated Sharpe ratio, point-in-time data, purged walk-forward. Searches published factor libraries and reports what actually survived costs.

Confidence interval simulations and hypothesis testing in R, with a practical study of selection and survivorship bias.

POSSA: Power simulation for sequential analyses and multiple hypotheses.