Statistical Methodology Papers of the Week

Building on classical case-control and matched case-control designs developed primarily for association inference with binary outcomes, we propose a risk-matched case-control (RMCC) design that uses a scalar disease-risk score constructed from Phase I variables to define matching strata and guide Phase II sampling.

Action-conditioned world models are increasingly trained on data from randomized experiments, and whether their predictions respond to actions is usually checked by replacing or removing the actions and measuring the loss in accuracy.

We introduce a framework using Gaussian processes trained on latent scores and prediction errors to estimate observation-specific bias and error variance, separating systematic error from epistemic and aleatoric variance.

We discuss methods for estimating triangular maps from finite samples, as well as their use in simulation-based inference and causal inference.

Adaptive enrichment trials that identify responsive subpopulations allocate a fixed sample across them and estimate each one's effect from the resulting data.

However, most existing works focus on pairwise conditional independence. To address this gap, we propose a kernel-based test of multi-way conditional independence.

We propose Uncertainty-Normalized Empirical Calibration (UNEC), a p-value-free framework that ranks candidates by uncertainty-normalized predictive margins and directly calibrates a selection cutoff using held-out losses.

Marginal permutation is exact for testing marginal independence but generally invalid for conditional independence because it breaks the dependence between the exposure ($\bm X$) and covariates ($\bm Z$). In this paper, we show that empirical studentization can nevertheless make this deliberately misspecified resampling scheme asymptotically valid for important conditional independence tests based on scores.

We introduce a latent variable representation which, combined with random Fourier features, yields a stochastic approximation expectation--maximization algorithm for penalized maximum likelihood estimation of a sparse inverse cross-spectral density matrix under a graphical LASSO penalty.

We propose CDaLCqr, a unified coordinate descent algorithm for penalized longitudinal log-contrast quantile regression accommodating popular penalties, such as adaptive Lasso, smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP) within a single framework.

A negative-binomial model describes total counts, while multinomial models describe how those counts are allocated among categories.

We prove that recovering all flipped comparisons from a single annotation per comparison requires margins of order $\log n$.

Listwise deletion (LWD) appears more common than imputation: our review of leading journals finds that about 50% of articles state or imply LWD, versus 7% describing an imputation model.

Motivated by the project on premature menopause among childhood cancer survivors in the Childhood Cancer Survivor Study (CCSS), we develop methods for estimating time-specific factor effects using doubly censored event time data with competing risks.

In this paper, we develop a projected instrumental variable framework that relaxes conventional assumptions for causal identification with instrumental variables.

In this paper, we develop design-based inference for the distribution of ITEs whose validity relies solely on the randomization of treatment assignment, treating the potential outcomes as arbitrary fixed constants.

We develop a Bayesian framework that infers structural competitive balance from longitudinal match outcomes.

We take the other route and make the statement hold for every interval at once.

Using a Dirichlet mixture as the reference model, we derive an exact representation of the mean integrated squared error (MISE) and define the exact MISE (EMI) selector.

We propose a unified, model-assisted covariate-adjustment framework for time-restricted marginal win statistics evaluated at a prespecified post-randomization restriction horizon, a clinically interpretable estimand particularly suited to acute and high-acuity trials with well-defined short-term follow-up windows.