Hypothesis Testing

All research related to Hypothesis Testing. A full list of topics is available on my research page.

  1. ⭐ Minimizing and quantifying uncertainty in AI-informed decisions: Applications in medicine

    Samuel D. Curtis*, Sambit Panda*, Adam Li*, Haoyin Xu, Yuxin Bai, Itsuki Ogihara, Eliza O’Reilly, Yuxuan Wang, Lisa Dobbyn, Maria Popoli, Janine Ptak, Nadine Nehme, Natalie Silliman, Jeanne Tie, Peter Gibbs, Lan T. Ho-Pham, Bich N. H. Tran, Thach S. Tran, Tuan V. Nguyen, Ehsan Irajizad, Michael Goggins, Christopher L. Wolfgang, Tian-Li Wang, Ie-Ming Shih, Amanda Fader, Anne Marie Lennon, Ralph H. Hruban, Chetan Bettegowda, Lucy Gilbert, Kenneth W. Kinzler, Nickolas Papadopoulos, Bert Vogelstein, Joshua T. Vogelstein, Christopher Douville
    PNAS, 2025

    Introduces MIGHT, which helps quantify the amount of predictive information in very high-dimensional data. This was then used to develop and evaluate a biomedical assay to detect cancer early.

  2. ⭐ Universally Consistent K-Sample Tests via Dependence Measures

    Sambit Panda*, Cencheng Shen*, Ronan Perry, Jelle Zorn, Antoine Lutz, Carey E. Priebe, Joshua T. Vogelstein
    Statistics & Probability Letters, 2025

    Introduces the idea that the k-sample testing problem and independence testing problem are equivalent up to a transformation of the data.

  3. 🎓 Random Forest for Hypothesis Testing: Development and Application to Cancer Detection

    Sambit Panda
    Johns Hopkins, 2024

    My PhD thesis, which discusses: (1) how to do k-sample testing via independence testing, (2) creation of the KMERF test, which uses the random forest induced kernel, and (3) introduce MIGHT and CoMIGHT that quantify information within datasets and apply it to a cancer dataset.

  4. 📝 hyppo: A Multivariate Hypothesis Testing Python Package

    Sambit Panda, Satish Palaniappan, Junhao Xiong, Eric W. Bridgeford, Ronak Mehta, Cencheng Shen, Joshua T. Vogelstein
    arXiv, 2024

    Introduces hyppo, a package that incorporates conventional and novel multivariate hypothesis tests.

  5. 📝 Learning sources of variability from high-dimensional observational studies

    Eric W. Bridgeford, Jaewon Chung, Brian Gilbert, Sambit Panda, Adam Li, Cencheng Shen, Alexandra Badea, Brian Caffo, Joshua T. Vogelstein
    arXiv, 2023

    Generalizes causal estimators to arbitrary dimensional space and uses this to develop a new test (Causal CDcorr).

  6. 📝 Learning Interpretable Characteristic Kernels via Decision Forests

    Sambit Panda*, Cencheng Shen*, Joshua T. Vogelstein
    arXiv, 2023

    Demonstrates the kernel derived from random forest is characteristic and develops a hypothesis test based on that fact (KMERF).

  7. 🖼️ Elucidating Relationships within Neurological Screening Batteries via Random Forest-Based Hypothesis Testing

    Sambit Panda, Leslie R. Wilson, Jariatu Stallone, Dalisa Kendricks, Korey Stevanovic, Jesse D. Cushman
    2023

    Applies a random forest based hypothesis test (specifically KMERF) to evaluate the effectiveness of a neurological screening test for mice.

  8. ⭐ The Chi-Square Test of Distance Correlation

    Cencheng Shen, Sambit Panda, Joshua T. Vogelstein
    JCGS, 2022

    Derives an approximation to the p-value of distance correlation that bypasses the permutation test with no significant loss of power.

  9. 🎓 Multivariate Independence and k-sample Testing

    Sambit Panda
    Johns Hopkins, 2020

    My master’s thesis, which introduces a Python package and a new framework for k-sample testing.