stat.ME · 2026-09-10 · No. 111

Methodology, 2026-09-10.

3 new papers in stat.ME. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Likelihood-free inference with nuisance parameters through normalizing flows

    Phil Assheton

    stat.ME · cs.LG · stat.ML

    We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a sample generator from the distribution of interest. We show that the statistic is near-pivotal in the sense of minimum average KL-divergence of its $p$-values versus uniform and we argue that it can be expected to have good power when the dimension...

    arxiv.org/abs/2609.10534 · PDF

  2. 02

    Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation

    Hiroshi Tamano, Daichi Mochihashi

    stat.ME · cs.LG

    Multidimensional item response theory (MIRT) is a statistical test theory that precisely estimates multiple latent skills of learners from the responses in a test. Both compensatory and non-compensatory models have been proposed for MIRT: the former assumes that each skill can complement other skills, whereas the latter assumes they cannot. This non-compensatory assumption is convincing in many tests that measure multiple skills; therefore,...

    arxiv.org/abs/2609.10028 · PDF

  3. 03

    Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking

    Duncan Stewardson, Grayson W. White, Adam Groce

    stat.ME · cs.CR · cs.LG

    Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields. These fields often work with sensitive data where privacy protections are important, so a differentially private mechanism for ATE estimation is highly desirable. Here we present two propensity score-based algorithms for ATE estimation on observational data, one improving the...

    arxiv.org/abs/2609.09536 · PDF

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