The paper introduces a new inference framework for least angle regression (LAR), clarifying its behavior and providing a formal stopping rule. It shows that LAR estimates non‑zero population correlations as independent normal variables, while zero correlations follow a non‑normal joint distribution, enabling variable importance interpretation. A modified bootstrap method is proposed to quantify uncertainty and assess variable entry, with results validated by simulation and data examples.
arXiv:2609.08671v1 Announce Type: cross Abstract: In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are ob...
arXiv:2411.02771v3 Announce Type: replace-cross Abstract: Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While c...
arXiv:2505. 20178v2 Announce Type: replace-cross Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation.
The paper introduces Pro-Active Inference (PAI), a method for efficiently estimating a population mean when only a subset of outcomes can be observed. PAI proactively selects which observation to label next from a covariate pool, updating machine‑learning predictions online, and provides finite‑sample unbiasedness, asymptotic validity, and an improved variance estimator. Experiments on eight datasets show that PAI outperforms Sequential Active Inference in most settings, while diagnostics reveal when additional adaptivity is beneficial or detrimental.
arXiv:2411.09686v4 Announce Type: replace Abstract: Regressing a function $F$ on $\mathbb{R}^d$ without incurring the statistical and computational curse of dimensionality requires exploitable struct...
arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
arXiv:2602. 16061v2 Announce Type: replace-cross Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard estimators are biased and the estimand is not identified without additional assumptions.
arXiv:2610.32178v1 Announce Type: new Abstract: Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty. We study this question...
arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.
arXiv:2610.19351v1 Announce Type: cross Abstract: In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure out...
The paper introduces Agentic Distributional Regression (ADR), a method that uses a language‑model agent to generate a short program estimating conditional densities from iid data. The agent constructs the estimator from a library of probability distributions, producing a parametric, mixture, or multivariate factorization whose parameters are explicit functions of covariates. The authors provide finite‑sample error bounds, an oracle inequality for model selection, and show asymptotic normality of fitted constants, with simulation results indicating ADR outperforms other methods on twelve real datasets and a new post‑training dataset.