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ArXiv Paperboy (Stat.ME+Econ.EM)

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posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it. maintainer: @apoorvalal.com source code: https://github.com/apoorvalal/bsky_paperbot

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ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2h ago

arXiv📈🤖 Automated threshold selection and associated inference uncertainty for univariate extremes By

arXiv:2310.17999v3 Announce Type: replace
Abstract: Threshold selection is a fundamental problem in any threshold-based extreme value analysis. While models are asymptotically motivated, selecting an appropriate threshold for finite samples is difficult and highly subjective through standard methods. Inference for high quantiles can also be highly sensitive to the choice of threshold. Too low a threshold choice leads to bias in the fit of the extreme value model, while too high a choice leads to unnecessary additional uncertainty in the estimation of model parameters. We develop a novel methodology for automated threshold selection that directly tackles this bias-variance trade-off. We also develop a method to account for the uncertainty in the threshold estimation and propagate this uncertainty through to high quantile inference. Through a simulation study, we demonstrate the effectiveness of our method for threshold selection and subsequent extreme quantile estimation, relative to the leading existing methods, and show how the method's effectiveness is not sensitive to the tuning parameters. We apply our method to the well-known, troublesome example of the River Nidd dataset.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 15h ago

arXiv📈🤖 A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk By Bernoussi, Arrouchi

This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and fire frequency. The study highlights the comparative performance of each technique and advocates for a hybrid strategy that combines their complementary strengths to enhance both the accuracy and interpretability of forecasts for extreme events.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 20h ago

arXiv📈🤖 Asymptotic Behavior of Principal Component Projections for Multivariate Extremes By Drees

$The extremal dependence structure of a regularly varying $d$-dimensional random vector can be described by its angular measure. The standard nonparametric estimator of this measure is the empirical measure of the observed angles of the $k$ random vectors with largest norm, for a suitably chosen number $k$. Due to the curse of dimensionality, for moderate or large $d$, this estimator is often inaccurate. If the angular measure is concentrated on a vicinity of a lower dimensional subspace, then first projecting the data on a lower dimensional subspace obtained by a principal component analysis of the angles of extreme observations can substantially improve the performance of the estimator. We derive the asymptotic behavior of such PCA projections and the resulting excess risk. In particular, it is shown that, under mild conditions, the excess risk (as a function of $k$) decreases much faster than it was suggested by empirical risk bounds obtained in \cite{DS21}. Moreover, functional limit theorems for local empirical processes of the (empirical) reconstruction error of projections uniformly over neighborhoods of the true optimal projection are established. Based on these asymptotic results, we propose a data-driven method to select the dimension of the projection space. Finally, the finite sample performance of resulting estimators is examined in a simulation study.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · yesterday

arXiv📈🤖 Parametric ROC Analysis and Optimal Cutoff Selection under Scale Mixtures of Skew-Normal Distributions: A Decision-Theoretic Framework with Asymptotic Inference By Paula, Mouri\~no, Domingues

We study an optimal threshold functional arising in binary classification for continuous biomarkers. While the ROC curve summarizes discriminatory performance across all thresholds, practical threshold selection must also account for disease prevalence and asymmetric misclassification costs. The classical Youden index corresponds to a symmetric special case and may therefore be suboptimal in realistic decision settings. In addition, biomarker distributions in serological and immunological studies often display skewness and heavy tails, making Gaussian ROC models inadequate.
We develop a parametric framework for ROC analysis and optimal cutoff selection under the family of scale mixtures of skew-normal (SMSN) distributions, including the skew-normal and skew-t models. The ROC curve and AUC are estimated by plug-in maximum likelihood from separate group fits. The optimal cutoff is defined as the minimiser of a weighted misclassification risk, which yields a likelihood ratio equation extending the Youden criterion. Under a monotone likelihood ratio condition, we establish existence, uniqueness, and global optimality of the cutoff. We further study its local regularity as an implicitly defined functional of the model parameter and derive consistency, asymptotic normality, and a closed-form plug-in variance estimator. A central term in this variance is the local slope of the estimating equation at the optimal threshold, which acts as a local identifiability diagnostic.
Monte Carlo experiments across six scenarios show that the asymptotic approximation is accurate and that Wald confidence intervals attain near nominal coverage. An application to SARS-CoV-2 serological data illustrates that the proposed cutoff can differ substantially from the Youden threshold and may reduce estimated misclassification risk by up to 63% under asymmetric decision settings.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Causal Fingerprints Reveal Hidden Mechanisms in Partially-Observed Dynamical Systems By Jiang, Andreou, Chen

In partially-observed dynamical systems, distinct hidden mechanisms can produce similar observable summary statistics while explaining individual events differently. We introduce causal fingerprints: realization-dependent patterns that track how information about a hidden state is revealed through subsequent evolution. Within a prescribed dynamical model, we compare the filtering posterior, based on observations up to a given time, with the smoothing posterior, obtained after adding an expanding window of future observations. We quantify this retrospective information gain using Kullback-Leibler divergence and track its evolution as the future window of observational data expands. The fingerprint reveals both the strength and temporal reach of hidden influence, which can vary across events and their stages. Because the observed trajectory is held fixed while hidden-state inference depends on the candidate model, fingerprints reveal how competing dynamical mechanisms explain the same observations even when selected statistics agree. In linear Gaussian systems, we derive a characteristic fingerprint timescale for the temporal reach of hidden influence. Calibrated surrogates matching the observable mean, variance, and decorrelation time can still produce distinct fingerprints when feedback memory, hidden-noise memory, or hidden forcing timescales are misrepresented. Nonlinear examples reveal mechanism-dependent signatures for extreme bursts, oscillatory variability, and regime switching, including multiplicative instability versus additive forcing, deterministic nonlinear feedback versus state-dependent multiplicative noise, and intrinsic bistability versus hidden double-well forcing. Causal fingerprints therefore provide a time- and event-resolved diagnostic for asking not only whether a model reproduces observed statistics, but how its hidden dynamics explain individual events.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Multiplex coupling: Connecting the intellectual base and the topics of a research field By Aria, Cuccurullo, Spano

$Two documents can be close because they cite the same works or because they write about the same things, and the two kinds of proximity often disagree. Science mapping reads them in separate maps or merges them, as hybrid clustering does; either way the disagreement is lost. We introduce multiplex coupling, a framework that couples the same documents by their references and by their terms, keeps the two layers separate and analyses their relation. Similarities are standardised against a null model, and the agreement between the layers is measured on random pairs of documents. Consensus clustering partitions each layer into roots, groups sharing an intellectual base, and themes, groups sharing a topic. A test on the roots $\times$ themes table identifies branching roots and convergent themes, and pairs of roots are followed over time. Every structure is reported with its persistence across numbers of neighbours, which on synthetic data identifies most false branching. Applications to Web of Science, Scopus and OpenAlex collections show that the two layers carry largely complementary information, and reveal substantive and methodological bases that have grown into several topics. The framework is implemented in bibliometrix.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 conformalForecast: An R package for sequential conformal prediction in time series By Wang, Girolimetto

Reliable uncertainty quantification is central to modern forecasting. Conformal prediction offers model-agnostic intervals with coverage guarantee under exchangeability, but existing software often targets general prediction problems or individual time series procedures rather than integrated workflows for multistep forecasting applications. We introduce conformalForecast, an open-source R package for constructing, updating, and evaluating conformal prediction intervals for multistep time series forecasts. The package separates point forecasting from calibration, accepts forecasts from diverse sources through a common interface, and preserves horizon-specific forecast errors and calibration states. It implements several conformal calibration schemes spanning empirical-quantile, feedback-based, and dependence-aware approaches, together with tools for assessing coverage, interval width, and interval scores over time and across horizons. Fitted objects can be extended as new observations arrive while retaining previously issued intervals and avoiding recomputation at earlier forecast origins. An application to short-term electricity price forecasting across five Australian regions illustrates the workflow. Aggregate coverage is generally close to the nominal level, while rolling coverage and interval efficiency vary across methods and horizons. In the reported experiment, incremental updating reproduces full-reconstruction intervals under identical forecasting and calibration settings while substantially reducing computation. conformalForecast provides a reproducible and extensible framework linking conformal methodology with sequential forecasting practice.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Trial design considerations in the face of overdispersion and time-varying event rates: a meta-regression approach with an application in asthma By Chen, R\"over, Andreas et al

**Purpose:** Relative changes in event rates are a common primary endpoint in confirmatory asthma trials. Reliable assumptions about baseline event rates and extra-Poisson variability are essential for sample size planning. We synthesize placebo-group exacerbation data, evaluate calendar-time trends, and assess implications for trial design.
**Methods:** We systematically reviewed randomized controlled trials reporting recurrent asthma exacerbations and extracted control-group outcomes classified by placebo type or asthma severity. Hierarchical negative binomial models accommodated heterogeneous reporting formats and jointly modeled event rates and overdispersion. We compared separate subgroup models with covariate-based models and used meta-regression to adjust for calendar-time trends. Trial design implications were illustrated using the NAVIGATOR trial.
**Results:** Sixty-two trials met the inclusion criteria. The estimated relative change in annualized exacerbation rates was -1.8% per year (95% CrI: -7.5%, 4.3%), corresponding to a 16.5% decrease per decade (95% CrI: -54.2%, 52.2%). Substantial between-study heterogeneity in event rates and overdispersion was observed. Within-trial overdispersion was estimated as 1.53 (95% CrI: 0.60, 2.83). In an illustrative phase III design scenario, sample size requirements were sensitive to the target subgroup and uncertainty in the predicted event rate and overdispersion.
**Conclusion:** Event rates in clinical trials exhibit considerable heterogeneity and potential temporal variation, and overdispersion is a critical design parameter. Comprehensive modelling of recurrent-event endpoints allows for evidence-based and robust sample size calculations and trial design.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Online Quantile Omniprediction for Proper Losses By Kim, Gibbs, Mahoney et al

The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omniprediction. Prior work in this area has largely focused on binary prediction tasks, and existing results for multiclass and continuous outcomes typically suffer from slow error rates. In this paper, we consider forecasts expressed as a set of quantiles over a discrete but otherwise arbitrary set of probability levels. We develop sample-efficient algorithms for constructing forecasts that are accurate simultaneously across all proper losses that satisfy mild regularity conditions, where proper losses are those minimized by predicting the true quantiles. Our algorithms are online and can operate in a dynamic environment, while simultaneously taking multiple losses into account, and their forecasts offer the potential to provide diverse end-users with rich information for evaluating risk and informing a range of practical decision-making tasks. As an example, we demonstrate how our procedure can be used to ensemble quantile forecasts of COVID-19 hospitalizations made by experts during the pandemic.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Beyond Match Results: Bayesian Mediation of Ability, Performance, and Winning in Professional Tennis By Hu, Wang

Match results measure competitive ability, but they do not explain how ability is expressed during play. Match statistics offer a natural mediator, however performance is usually measured over the same match as the outcome: a full-match statistic uses events that help determine the winner, while a first-set statistic is earlier but rests on fewer service points and still contributes to the result. Comparing the two therefore mixes timing and precision. We develop a Bayesian mediation framework for this problem. Dynamic abilities on hard, clay, and grass courts are estimated from previous seasons only and are linked to both players' service performance, and a Bradley--Terry model relates ability and performance to the match result. A control mediator samples the first set's number of service points from the full match, separating the measurement window from the point count. In 3,820 men's matches, the hard-court mediated share rises from 0.52 with a first-set mediator to 0.95 with a full-match mediator; the point-count contrast accounts for 0.30 of this gap and the window contrast for 0.13. The pattern replicates on the women's tour. Simulations and sensitivity analyzes examine the decomposition and assumptions needed for a causal interpretation.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes By Brahma, Meng, Carin et al

Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses unmeasured confounding through proxy variables, but existing proximal survival methods rely largely on restrictive parametric bridge models. We introduce Proximal Survival Bridge learning (PSB), a flexible doubly robust proximal framework for censored outcomes. Building on existing proximal identification of treatment-specific survival curves, the framework yields outcome-bridge (OB), treatment-bridge (TB), and doubly robust (DR) estimators, with two flexible bridge learners: PSB-K, a kernel estimator with a low-rank time decomposition, and PSB-N, a neural estimator trained by a projected maximum-moment-restriction loss. Unlike existing flexible proximal learners for scalar outcomes, PSB directly handles censoring and survival-curve targets. On a standard SEM benchmark, a proximal semi-synthetic ACTG study, and the real-world Framingham study, PSB-K performs strongest on SEM and ACTG, whereas PSB-N is the most stable on Framingham. Additional results with modern causal survival baselines, weakened or invalid proxies, and heavier censoring assess robustness across challenging settings.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection By Chen, Adibi, Banerjee et al

A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection. A change is declared when a weighted sum of these statistics crosses a threshold. This framework is then applied to the problem of multi-stream QCD to obtain a family of powerful algorithms that outperform the best known algorithms in the literature. In the multi-stream QCD problem, a decision-maker observes multiple streams of observations. At the change point, the distribution of an unknown subset of the streams changes. The optimal algorithm based on a generalized likelihood ratio is computationally expensive. One of the most computationally and performance-efficient algorithms in the literature is the rank-weighted sum (RWS) scheme based on Shiryaev-Roberts statistics. It is shown in the paper that the MoE-based QCD algorithm for multi-stream data outperforms the RWS scheme. This is demonstrated through simulation results on real-valued Gaussian mean shift data and high-dimensional data from Gaussian--Bernoulli Restricted Boltzmann Machines (GB-RBMs).

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Precise Asymptotics for Dense High-Dimensional Huber Regression under Heteroscedasticity By Zhou, Venkataramanan, Zou

$We study Huber regression in a heteroscedastic linear model in the proportional regime $n/p \to \delta \in (1, \infty)$. Our main result is an exact characterization of the asymptotic empirical distribution of the Huber estimator. This characterization is in terms of three scalar parameters which determine the first-order centre and the asymptotic variance of the Huber estimator. The result shows that with heteroscedastic noise, the first-order centre may contain an additional component along the variance index coefficient. We derive the displacement coefficients that determine this centre, as well as the exact asymptotic variance. The analysis uses the matrix approximate message passing (AMP) framework to jointly track the regression and variance index coefficients, extending the existing framework for homoscedastic M-estimators. We illustrate the implications of this characterization by constructing a test for centre displacement and examining its empirical size and power through simulations.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Extending the Oracle Approximating Shrinkage to General Distributions By Lewis

$The Oracle Approximating Shrinkage (OAS) estimator provides a closed-form, data-driven shrinkage intensity for regularizing sample covariance matrices toward a scaled identity target, but its derivation assumes Gaussian data. In practice, financial returns, neural signals, and many other high-dimensional datasets exhibit heavy tails and non-zero kurtosis, violating this assumption. We extend OAS to any distribution with finite fourth moments by incorporating a correction term $\alpha$ built from the fourth-order joint cumulants of the data. The correction vanishes under Gaussianity, recovering the OAS formula as a special case; it admits a consistent $O(np)$ sample estimator and satisfies $\rho \in [0, 1]$ exactly for both the population cumulant and its sample analogue. Simulations and an equity-returns study confirm that the correction closes the gap between OAS and the Ledoit-Wolf estimator under non-Gaussian data, with OAS-Extended performing comparably across all tested settings. The contribution is a principled correction for OAS practitioners facing heavy-tailed data; structurally, it is distinct from prior work extending Ledoit-Wolf to elliptical distributions, which optimizes against a fixed oracle target and yields a different closed-form formula.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates By Dukes, Shahn, Robins et al

$We introduce a class of marginal Structural Nested Mean Models (mSNMMs) that enable modeling time-varying effect modification by a subset of covariates required as adjustment variables. We focus on identifiability and estimation under conditional parallel trends assumptions, so mSNMMs are applicable in settings where time-varying Difference-in-Differences (DiD) methods are typically applied. Modeling effect modification by lower dimensional covariates reduces the risk of model misspecification and can improve interpretability relative to previously developed `full SNMMs'. mSNMMs enjoy two key advantages over alternative time-varying DiD methods. First, the parallel trends assumptions that they require are more plausible in the presence of time-varying confounding. Second, they are able to model effects of \textit{sustained} interventions even when treatment switches on and off in the data. We illustrate the methodology by applying mSNMMs to estimate how the effects of Medicaid expansion (a time-varying treatment) on local uninsurance rates vary with unemployment rate prior to expansion (a time-varying covariate), adjusting for additional time-varying demographic and economic variables.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Clustering extremal dependence structures via Wasserstein distances between angular measures By Gasser, Ragone, Segers

Compound extreme events cause damage out of proportion to their frequency, and grouping random vectors by their joint extremal behaviour is a recurring task in climate science, hydrology, and finance. Classical clustering is driven by typical observations, while existing extremal procedures either summarize dependence by a single coefficient or discretize the simplex in an ad hoc way. We propose a dissimilarity that compares complete angular measures by optimal transport: each angular measure is estimated above a high radial threshold, mapped from the simplex into a Euclidean space of Aitchison coordinates, and compared by the 1-Wasserstein distance. The methodology is fully nonparametric and requires no fitted model. The dissimilarities feed any distance-based clustering algorithm. Under intermediate-threshold asymptotics and mild moment and separation conditions, hierarchical clustering and partitioning around medoids based on them recovers the latent partition with probability tending to one. In simulations, the proposed approach matches established competitors when clusters differ mainly in the strength of extremal dependence and performs substantially better when equal-strength clusters differ in the shape of their angular measures, especially when asymmetry drives the separation. Applications to rain--snowmelt--runoff extremes in Swiss catchments and to extremal co-movement between international equity markets and the US market reveal hydrologically and economically interpretable groups.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Prescreen, Then Subsample: Efficient model training for large-scale biomedical data with correlated covariates By Dasgupta, Staerk

Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omics applications such as genomic studies. Analyzing these large-scale datasets imposes substantial computational and memory demands, requiring efficient model training strategies that maintain predictive accuracy. We propose Prescreen, Then Subsample (PTS), a two-stage approach that combines variable selection with optimal-design-based informative subsampling to address both high dimensionality and large sample size. The first stage screens informative variables and can explicitly account for covariate correlation; the second stage selects informative observations in the prescreened variable space. In contrast to existing two-stage subdata selection approaches, PTS can incorporate multivariable fine-mapping to identify a correlation-aware covariate space for informative subsampling. In particular, we integrate the Sum of Single Effects (SuSiE) model for fine-mapping-based prescreening with information-based optimal subdata selection (IBOSS) for sample selection. Simulation studies based on correlated Gaussian covariates and biobank-scale synthetic genotype data show that PTS reduces model training time while achieving predictive performance comparable to full-data training. We find that SuSiE-based PTS provides improved predictive performance consistent with better stage-one screening precision compared with commonly used univariate prescreening methods. PTS therefore provides a flexible and scalable training framework for large-scale biomedical data with complex correlation structures, including genomic data with linkage disequilibrium.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Covariance-Calibrated Prediction-Powered Inference By Chen, Bai, Zhu

Covariance-calibrated prediction-powered inference (CC-PPI) uses unlabeled prediction scores to calibrate the covariance weight in a GMM estimator. The raw target preserves the prediction-corrected channel and replaces the labeled prediction-covariance contribution in the auxiliary residual metric with its unlabeled counterpart. Spectral protection ensures positive definiteness, and the estimator uses an equal blend of the original covariance and the protected target. We establish first-order efficiency and derive exact finite-sample risk identities for linear regression, including a design-based improvement theorem with tolerance to prediction error. A quadratic decision model gives a midpoint minimax-regret interpretation of the half-strength default. Exact strength-path formulas and convergent risk approximations bound its distance from the best fixed strength on that path. Simulations and four real-data studies show substantial reductions in parameter MSE. Independent fixed-strength references yield only small further gains on the calibration paths studied.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Locally Robust Inference with Outcomes Predicted by Machine Learning By Escanciano, Puerta-Cuartas

Many parameters of interest in economics depend on outcomes generated by machine learning (ML).These generated outcomes are subsequently used in standard econometric estimation and typically treated as observed, a practice that threatens the validity of downstream inference. Empirically relevant cases include dynamic treatment effects under sequential exogeneity, long-term effects with surrogates, the partially linear model with an ML-predicted outcome, productivity persistence with an unrestricted production function and constructed instruments, and outcomes residualized of confounding variation. This paper develops a three-step locally robust GMM framework for valid inference when the dependent variable is generated by ML. We construct a debiased moment condition that augments the identifying moment with first- and second-step influence functions, making inference on the parameter of interest locally insensitive to regularization and model selection biases at both steps. We establish consistency and asymptotic normality of the proposed estimator and illustrate its finite-sample performance through Monte Carlo simulations. We also apply the method to U.S. mortgage data to study conditional Black--White disparities in a race-blind ML default-risk score. The score predicts subsequent default well overall, but the conditional disparity embedded in the score is much smaller than the corresponding disparity in realized serious delinquency; accounting for first-step estimation also raises the naive out-of-sample estimate of the score disparity.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Benchmarking Priors: A Fully Bayesian Approach to Small Area Benchmarking By Bhowmick, Roy, Janicki et al

We develop a new class of priors for Bayesian small area models under strict external bench- marking constraints. The prior is fully supported on the constrained parameter set defined by the benchmarking constraints and thereby provides a proper constrained posterior that is fully supported on the restricted set. The posterior naturally constrains the small area estimates to conform to the benchmarking values at the aggregate level. In addition, it facilitates valid uncertainty quantification using credible sets that are fully contained within the constrained set. This novel framework has a wide range of applicability, ranging from the standard Gaus- sian Fay-Herriot model to more general Fay-Herriot models, such as robust Fay-Herriot models, where the area-level random effects are distributed as multivariate t, or Fay-Herriot models where there is a complex nonlinear relationship between the area means and the covariates. We illustrate the usefulness of the proposed methodology using simulation experiments as well as by estimating the state-level proportion of children aged 5-17 living under poverty based on the direct estimates obtained from the Small Area Income and Poverty Estimates (SAIPE) program of the U.S. Census Bureau and the national benchmark of the child poverty rate obtained from the Annual Social and Economic Supplement of the Current Population Survey (CPS ASEC) conducted by the U.S. Census Bureau. The numerical results reaffirm the advantages of the proposed benchmarking priors.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Calibrated Transfer Learning for Survival Prediction and Treatment Inference By Wang, Zhang, Yang et al

$Historical survival data can improve prediction in a cohort with few events, but prognostic differences can make borrowing harmful. To borrow while adapting, we propose calibrated transfer learning for the Cox model, in which a historical risk function enters the target analysis as a fixed offset with a prespecified correction for changing covariate effects and a separate baseline hazard. Calibration removes historical error in the correction space exactly, so only the remaining error governs the prediction rate, for any bounded learner. Accurate prediction does not, however, ensure valid treatment inference. In randomized trials, the remaining error biases the conditional treatment coefficient, because a treatment effect changes the prognostic composition of later risk sets. We characterize this bias and its sharp worst-case direction, and we introduce a cross-fitted projected score that removes it at first order and leaves only a second-order bias when a fixed error lies in its dictionary. A historical learning bound separates approximation, sampling, and optimization error. In simulations, calibration improves prediction when the historical risk is well learned, and the projected score restores interval coverage under drift outside the correction, also at a hazard ratio of 0.7. In a retrospective transfer between the POPLAR and OAK lung cancer trials, selected corrections reduce the integrated Brier score by 4.3\% relative to ridge-penalized clinical Cox with 100 training patients, without improving treatment precision.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Controlling the False Discovery Rate via Model-Agnostic Importance Measures By Wen, Ren, Zou

Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Traditional variable selection methods often rely on specific model assumptions, while Model-X knockoffs provide a general way that leverages knowledge of the covariate distribution to produce a variable selection algorithm. This study puts forward a complementary method to Model-X knockoffs, named Model-Agnostic Selection with agent-X importance (MAS), which utilizes the sample-splitting strategy and a novel variable importance measure to bypass distributional assumptions on covariates. The proposed model-agnostic agent-X importance is defined to evaluate the difference of oracle predictiveness on both the original distribution and the distribution with agent variable in population level, and can be efficiently estimated by constructing the agent variable as some constant and cross-fitting strategy. The MAS method integrates a resplit-per-covariate strategy to decouple estimator dependencies and a data aggregation technique to enhance power. Theoretical guarantees for asymptotic FDR control are established under mild conditions. Extensive simulations and real-data analyses demonstrate that MAS provides competitive performances with existing works in FDR control, computational efficiency and power.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Causal Mediation Analysis for Panel Data with Double/Debiased Machine Learning By Wu, Xiao

We develop a double/debiased machine learning framework for causal mediation analysis in panel data with two-way fixed effects. Binary, multivalued, or continuous treatments and continuous mediators affect outcomes via histories. We formulate partially linear models for potential outcomes and mediators, define history-specific direct, indirect, and total effects, and identify them under a sequential conditional independence assumption given unit effects, allowing arbitrary time-invariant confounding. Estimation combines first-differencing, cross-fitting sample splitting that removes period effects, and machine-learned confounding functions within a GMM framework whose score is Neyman orthogonal in all nuisance components. We establish asymptotic normality of the estimators for the structural parameters and causal estimands and provide consistent variance estimators. Simulations show small bias and near-nominal coverage under nonlinear confounding, whereas a DML-free benchmark fails severely for indirect effects. An application to U.S. county data illustrates that a stable total effect can conceal substantial sensitivity in its direct-indirect decomposition.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Reconstructing constant point heat sources from few temperature sensors: sparse selection and spatial resolution By Yang, Deng

Thermal diffusion and measurement noise can obscure the number and individual parameters of localized heat sources. We study the reconstruction of constant positive point sources from temperature histories at a few sensors, without prescribing the source count. Orthogonal matching pursuit or least angle regression selects candidate locations from a unit-response dictionary. These candidates initialize continuous constrained fits, and a noise-based discrepancy rule selects the smallest accepted model among the attempted fits. The response analysis distinguishes rejection of a merged one-source model from accurate recovery of the individual sources. It gives a projected quadratic residual for a close pair, a weak direction in the two-source parameters, and a conditional bound for recovering the total strength and strength-weighted center of an unresolved cluster. A local Gaussian approximation relates one-source rejection to noise and separation. Paired numerical tests compare sensor and time-sampling budgets, noise levels, and source separations. Finite-plate data generated independently of the inverse response and a three-dimensional example assess the same fitting procedure. In the tested configurations, source-count selection and individual parameter recovery differ substantially, while unresolved pairs can retain accurate aggregate estimates. The framework combines reconstruction with a local interpretation of which heat-source information the observations support.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 On the fairness of single round-robin tournaments: Evidence from European football By Csat\'o, F\H{u}r\'esz, Havran

Single round-robin tournaments are widely regarded as unfair because of their home-away imbalance: some competitors play more home games than the others. Therefore, most sports leagues are played in a double round-robin format. However, some European football leagues contain a single round-robin (sub)tournament. To test whether empirical data can validate their unfairness, we construct a unique database and develop a strength of schedule metric based on Elo ratings with favourable theoretical properties. In contrast to the robust impact of relative team strength, the strength of schedule is found to have no significant effect on the number of points scored under the assumption of a league-specific home advantage. Consequently, the theoretical unfairness is not a serious issue in practice, or the standard approach of modelling home advantage in Elo ratings might need to be reconsidered.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Bayesian Nonparametric Longitudinal Factor Model for Microbiome Counts By Zhang, Ok, Jayaraman et al

Longitudinal microbiome studies provide important insights into the temporal evolution of microbial abundances and dynamic cross-feature dependence. Modeling longitudinal microbiome data is challenging because microbiome counts are highly heterogeneous and zero-inflated, and, moreover, repeated measurements induce within-subject temporal dependence and possibly time-varying cross-feature covariance. To address these challenges, we develop a flexible Bayesian longitudinal factor model using a rounded log-normal latent representation. The mean abundance process begins with a Dirichlet process mixture baseline, which accommodates sparse and multimodal abundance distributions, and subsequently evolves through an autoregressive process. Autoregressive coefficients differ across prespecified biological groups, which are defined by genotypes in our application. A separate autoregressive factor process captures shared cross-feature dependence. The model therefore distinguishes subject--feature mean evolution from shared dependence and yields a closed-form, time-indexed covariance. Simulation studies evaluate dynamic covariance recovery under matched and misspecified data-generating mechanisms. In the longitudinal mouse-gut microbiome application, LODI identifies highly persistent feature-specific abundance trajectories and shared latent factors that distinguish the three genotype groups over time. At week 15, the dominant factor follows the observed WT < ACK/G < HACK/G tumor-severity ordering, with its strongest contributions arising from OTUs classified within Clostridiaceae, Clostridium, and RF39.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Using Heteroscedasticity for Causal Discovery under Hidden Confounding By Zou, Li, Lin

The presence of hidden confounding in observational studies poses a fundamental challenge to the identification and estimation of causal structures encoded by directed acyclic graphs (DAGs). While instrumental variables (IVs) have been widely used to address hidden confounding, most existing methods require valid instruments, which may be unavailable in practice. In this paper, we develop a novel framework for causal discovery and inference in DAGs that accommodates both hidden confounding and invalid IVs by exploiting heteroscedasticity. We establish a novel linear characterization of leaf nodes through heteroscedasticity-induced relationships between cross-covariance and self-covariance vectors, enabling recursive recovery of the causal order without requiring any candidate IV to be valid. Given the identified order, we then sequentially construct surrogate IVs to identify the causal effects and directions, thereby recovering the entire DAG. We also propose a data-adaptive procedure for estimating the DAG and causal effects. We show that the proposed method consistently recovers the underlying DAG and yields asymptotically normal estimators of causal effects. Extensive simulation studies demonstrate substantial improvements over existing methods. We further illustrate the practical utility of the proposed method through an application to gene regulatory network analysis.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Dimension reduction of compositional data through mixtures of latent source compositions By Lee, Marron, Grunsky et al

Understanding the distribution of mineral compositions is an important challenge in geology. This motivates new variations on dimension reduction for compositional data, lying on a simplex, that approximate observations with lower-dimensional subsimplices. This representation interprets compositions as convex combinations of fewer number of latent source compositions, providing unique insight into the underlying data-generating processes. We develop this approach within the framework of backwards principal component analysis, which constructs nested approximating structures by successive dimension reduction by rank one. We introduce a class of subsimplices, termed proper subsimplices, and provide a formal definition together with a systematic study of their geometric properties. Proper subsimplices admit effective parametrization via hyperplanes, while providing a more flexible approximation than subsimplices obtained by amalgamation. We characterize simple conditions under which hyperplanes generate proper subsimplices. Based on these conditions, we propose both an exact algorithm and a computationally efficient approximate algorithm for estimating proper subsimplex representations. The usefulness of this method is demonstrated on two geochemical datasets where it gives improved insights compared to the conventional PCA approach. Main ideas are further illustrated in a toy example and a simulation study.

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Strong Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in $r$-Largest Generalized Extreme-Value Models By Park, Shin, Park

$We study maximum likelihood estimation in the three-parameter $r$-largest generalized extreme-value ($r$GEV) model with a fixed number $r$ of upper order statistics per block. Because the support depends on all three parameters, the classical regular theory does not apply. We show that the maximum likelihood estimator is strongly consistent on compact parameter sets contained in $k<1$, where $k=-\gamma$ denotes the shape parameter. For $k_0<1/2$, the range in which the model is differentiable in quadratic mean, the estimator is root-$n$ consistent, asymptotically normal and asymptotically efficient. The proof verifies the conditions of the moving-support likelihood theorem of B\"ucher and Segers for the ordered $r$-dimensional likelihood; the main new ingredients are a sequential modified likelihood criterion and a support-loss bound showing that the regularity threshold $k_0<1/2$ does not depend on $r$. In simulated heavy-tailed cases with only $50$ blocks, using five to seven order statistics instead of the block maximum reduces the relative bias of the 100-year return level from $6$--$13\%$ to below $5\%$.$

ArXiv Paperboy (Stat.ME+Econ.EM)@paperposterbot.bsky.social · 2d ago

arXiv📈🤖 Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process By Kulkarni

We characterize the multiplicative half-Cauchy process through its log-scale random-walk representation, establishing shrinkage, expansion, and critical regimes, almost-sure summability, local rebounds, and effective dimension. We also derive a semi-analytic calibration rule linking interpretable prior probability statements to its scale parameter.