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paperposterbot.bsky.social
ArXiv Paperboy (Stat.ME+Econ.EM)
@paperposterbot.bsky.social
2.1K followers 2 following 20K posts
posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it.

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arXiv📈🤖
Joint survival annuity derivative valuation in the linear-rational Wishart mortality model
By Fonseca, Wong
This study proposes a linear-rational joint survival mortality model based on the Wishart process. The Wishart process, which is a stochastic continuous matrix affine process, allows for a general dependency between the mortality intensities that are constructed to be positive. Using the linear-rational framework along with the Wishart process as state variable, we derive a closed-form expression for the joint survival annuity, as well as the guaranteed joint survival annuity option. Exploiting our parameterisation of the Wishart process, we explicit the distribution of the mortality intensities and their dependency. We provide the distribution (density and cumulative distribution) of the joint survival annuity. We also develop some polynomial expansions for the underlying state variable that lead to fast and accurate approximations for the guaranteed joint survival annuity option. These polynomial expansions also significantly simplify the implementation of the model. Overall, the linear-rational Wishart mortality model provides a flexible and unified framework for modelling and managing joint mortality risk.
February 9, 2026 at 8:10 PM
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arXiv📈🤖
Modeling the Hazard Function with Non-linear Systems in Dynamical Survival Analysis
By Liyanage, Hridoy, Mostafa
Hazard functions play a central role in survival analysis, offering insight into the underlying risk dynamics of time to event data, with broad applications in medicine, epidemiology, and related fields. First order ordinary differential equation (ODE) formulations of the hazard function have been explored as extensions beyond classical parametric models. However, such approaches typically produce monotonic hazard patterns, limiting their ability to represent oscillatory behavior, nonlinear damping, or coupled growth decay dynamics. We propose a new statistical framework for modeling and simulating hazard functions governed by higher-order ODEs, allowing risk to depend on both its current level, its rate of change, and time. This class of models captures complex time dependent risk behaviors relevant to survival analysis and reliability studies. We develop a simulation procedure by reformulating the higher order ODE as a system of nonlinear first order equations solved numerically, with failure times generated via cumulative hazard inversion. Likelihood based inference under right censoring is also developed, and moment generating function analysis is used to characterize tail behavior. The proposed framework is evaluated through simulation studies and illustrated using real world survival data, where oscillatory hazard dynamics capture temporal risk patterns beyond standard monotone models.
February 9, 2026 at 8:07 PM
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arXiv📈🤖
Assessment of evidence against homogeneity in exhaustive subgroup treatment effect plots
By Bornkamp, Lu, Bretz
Exhaustive subgroup treatment effect plots are constructed by displaying all subgroup treatment effects of interest against subgroup sample size, providing a useful overview of the observed treatment effect heterogeneity in a clinical trial. As in any exploratory subgroup analysis, however, the observed estimates suffer from small sample sizes and multiplicity issues. To facilitate more interpretable exploratory assessments, this paper introduces a computationally efficient method to generate homogeneity regions within exhaustive subgroup treatment effect plots. Using the Doubly Robust (DR) learner, pseudo-outcomes are used to estimate subgroup effects and derive reference distributions, quantifying how surprising observed heterogeneity is under a homogeneous effects model. Explicit formulas are derived for the homogeneity region and different methods for calculation of the critical values are compared. The method is illustrated with a cardiovascular trial and evaluated via simulation, showing well-calibrated inference and improved performance over standard approaches using simple differences of observed group means.
February 9, 2026 at 8:04 PM
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arXiv📈🤖
Identification and Estimation of Network Models with Nonparametric Unobserved Heterogeneity
By Zeleneev
Homophily based on observables is widespread in networks. Therefore, homophily based on unobservables (fixed effects) is also likely to be an important determinant of the interaction outcomes. Failing to properly account for latent homophily (and other complex forms of unobserved heterogeneity) can result in inconsistent estimators and misleading policy implications. To address this concern, we consider a network model with nonparametric unobserved heterogeneity, leaving the role of the fixed effects unspecified. We argue that the interaction outcomes can be used to identify agents with the same values of the fixed effects. The variation in the observed characteristics of such agents allows us to identify the effects of the covariates, while controlling for the fixed effects. Building on these ideas, we construct several estimators of the parameters of interest and characterize their large sample properties. Numerical experiments illustrate the usefulness of the suggested approaches and support the asymptotic theory.
February 9, 2026 at 8:00 PM
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arXiv📈🤖
A prediction interval for the population-wise error rate
By Luschei, Brannath
We construct an asymptotic prediction interval for the population-wise error rate (PWER), which is a multiple type I error criterion for clinical trials with overlapping patient populations. The PWER is the probability that a randomly selected patient will receive an ineffective treatment. It must usually be estimated due to unknown population strata sizes, such that only an estimate can be controlled at the given significance level. We apply the delta method to find a prediction interval for the resulting true PWER, we demonstrate by simulations that the interval has the required coverage probability, and illustrate the approach with real data examples.
February 9, 2026 at 7:57 PM
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arXiv📈🤖
Efficient Online Variational Estimation via Monte Carlo Sampling
By STOCKHOLM), STOCKHOLM), (UBS) et al
This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models.
February 9, 2026 at 7:54 PM
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arXiv📈🤖
On Stein's Method of Moments and Generalized Score Matching
By Kume, Walker
We show that a special case of method of moment estimator derived from the Stein class coincides with the class of generalized score matching estimator. Choosing a suitable weight function for generalized score matching is not straightforward. However, by placing it within the method of moment framework we can alleviate this problem by extending the Stein class to generalized method of moments. As a consequence we can work with a number of functions and hence derive generalized score matching estimators with optimal properties.
February 9, 2026 at 7:52 PM
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arXiv📈🤖
Social Interactions Models with Latent Structures
By Lin, Shi, Zheng
This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and determine the number of clusters. To solve the incidental parameter problem in the binary choice model with social interactions, we propose a parametric bootstrap method to debias and establish its asymptotic validity. Monte Carlo simulations confirm strong finite sample performance of our methods. In an application to students' risky behaviors, the algorithm detects two latent clusters and finds that peer effects are significant within one of the clusters, demonstrating the practical applicability in uncovering heterogeneous social interactions.
February 9, 2026 at 7:48 PM
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arXiv📈🤖
E-values for Adaptive Clinical Trials: Anytime-Valid Monitoring in Practice
By Sokolova, Sokolov
Adaptive clinical trials rely on interim analyses, flexible stopping, and data-dependent design modifications that complicate statistical guarantees when fixed-horizon test statistics are repeatedly inspected or reused after adaptations. E-values and e-processes provide anytime-valid tests and confidence sequences that remain valid under optional stopping and optional continuation without requiring a prespecified monitoring schedule. This paper is a methodology guide for practitioners. We develop the betting-martingale construction of e-processes for two-arm randomized controlled trials, show how e-values naturally handle composite null hypotheses and support futility monitoring, and provide guidance on when e-values are appropriate, when established alternatives are preferable, and how to integrate e-value monitoring with group sequential and Bayesian adaptive workflows. A numerical study compares five monitoring rules -- naive and calibrated versions of frequentist, Bayesian, and e-value approaches -- in a two-arm binary-endpoint trial. Naive repeated testing and naive posterior thresholds inflate Type I error substantially under frequent interim looks. Among the valid methods, the calibrated group sequential rule achieves the highest power, the e-value rule provides robust anytime-valid control with moderate power, and the calibrated Bayesian rule is the most conservative. Extended simulations show that the power gap between group sequential and e-value methods depends on the monitoring schedule and reverses under continuous monitoring. The methodology, including futility monitoring, platform trial multiplicity control, and hybrid strategies combining e-values with established methods, is implemented in the open-source R package `evalinger` and situated within the regulatory framework of the January 2026 FDA draft guidance on Bayesian methodology.
February 9, 2026 at 7:44 PM
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arXiv📈🤖
Design-Conditional Prior Elicitation for Dirichlet Process Mixtures: A Unified Framework for Cluster Counts and Weight Control
By Lee
Dirichlet process mixture (DPM) models are widely used for semiparametric Bayesian analysis in educational and behavioral research, yet specifying the concentration parameter remains a critical barrier. Default hyperpriors often impose strong, unintended assumptions about clustering, while existing calibration methods based on cluster counts suffer from computational inefficiency and fail to control the distribution of mixture weights. This article introduces Design-Conditional Elicitation (DCE), a unified framework that translates practitioner beliefs about cluster structure into coherent Gamma hyperpriors for a fixed design size J. DCE makes three contributions. First, it solves the computational bottleneck using Two-Stage Moment Matching (TSMM), which couples a closed-form approximation with an exact Newton refinement to calibrate hyperparameters without grid search. Second, addressing the "unintended prior" phenomenon, DCE incorporates a Dual-Anchor protocol to diagnose and optionally constrain the risk of weight dominance while transparently reporting the resulting trade-off against cluster-count fidelity. Third, the complete workflow is implemented in the open-source DPprior R package with reproducible diagnostics and a reporting checklist. Simulation studies demonstrate that common defaults such as Gamma(1, 1) induce posterior collapse rates exceeding 60% regardless of the true cluster structure, while DCE-calibrated priors substantially reduce bias and improve recovery across varying levels of data informativeness.
February 9, 2026 at 7:39 PM
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arXiv📈🤖
Conformal changepoint localization
By Hore, Ramdas
We study the problem of offline changepoint localization in a distribution-free setting. One observes a vector of data with a single changepoint, assuming that the data before and after the changepoint are iid (or more generally exchangeable) from arbitrary and unknown distributions. The goal is to produce a finite-sample confidence set for the index at which the change occurs without making any other assumptions. Existing methods often rely on parametric assumptions, tail conditions, or asymptotic approximations, or only produce point estimates. In contrast, our distribution-free algorithm, CONformal CHangepoint localization (CONCH), only leverages exchangeability arguments to construct confidence sets with finite sample coverage. By proving a conformal Neyman-Pearson lemma, we derive principled score functions that yield informative (small) sets. Moreover, with such score functions, the normalized length of the confidence set shrinks to zero under weak assumptions. We also establish a universality result showing that any distribution-free changepoint localization method must be an instance of CONCH. Experiments suggest that CONCH delivers precise confidence sets even in challenging settings involving images or text.
February 9, 2026 at 7:37 PM
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arXiv📈🤖
Latent variation in pathogen strain-specific effects under multiple-versions-of-treatment theory
By Gon\c{c}alves
Evidence-informed policy on infections requires estimates of their effects on health. However, pathogenic variation, whereby occurrence of adverse outcomes depends on the infecting strain, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies on effects of infections on health when there is heterogeneity in strain-specific effects and information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for non-compound exposures. This discussion, that considers potential heterogeneity in strain-specific effects, will facilitate interpretation of these studies, and for the reasons mentioned above, also highlights the value of pathogen subtype data.
February 9, 2026 at 7:33 PM
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arXiv📈🤖
A Compound Logistic Regression Model for Binary Responses
By Almudevar, Almudevar
Logistic regression is the most commonly used method for constructing predictive models for binary responses. One significant drawback to this approach, however, is that the asymptotes of the logistic response function are fixed at 0 and 1, and there are many applications for which this constraint is inappropriate. More flexible models have been proposed for this application, most proceeding by supplementing the logistic response function with additional parameters. In this article we extend these models to allow correlated responses and the inclusion of covariates. This is achieved through the \emph{compound logistic regression model}, for which the mean response is a function of several logistic regression functions. This permits a greater variety of models, while retaining the advantages of logistic regression.
February 9, 2026 at 7:32 PM
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arXiv📈🤖
Mosaic inference on panel data
By Spector, Barber, Cand\`es
Analysis of panel data via linear regression is widespread across disciplines. To perform statistical inference, such analyses typically assume that clusters of observations are jointly independent. For example, one might assume that observations in New York are independent of observations in New Jersey. Are such assumptions plausible? Might there be hidden dependencies between nearby clusters? This paper introduces a mosaic permutation test that can (i) test the cluster-independence assumption and (ii) produce confidence intervals for linear models without assuming the full cluster-independence assumption. The key idea behind our method is to apply a permutation test to carefully constructed residual estimates that obey the same invariances as the true errors. As a result, our method yields finite-sample valid inferences under a mild "local exchangeability" condition. This condition differs from the typical cluster-independence assumption, as neither assumption implies the other. Furthermore, our method is asymptotically valid under cluster-independence (with no exchangeability assumptions). Together, these results show our method is valid under assumptions that are arguably weaker than the assumptions underlying many classical methods. In experiments on well-studied datasets from the literature, we find that many existing methods produce variance estimates that are up to five times too small, whereas mosaic methods produce reliable results. We implement our methods in the python package mosaicperm.
February 9, 2026 at 3:56 AM
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arXiv📈🤖
Distributed Online Economic Dispatch With Time-Varying Coupled Inequality Constraints
By Zhou, Wang, Li
We investigate the distributed online economic dispatch problem for power systems with time-varying coupled inequality constraints. The problem is formulated as a distributed online optimization problem in a multi-agent system. At each time step, each agent only observes its own instantaneous objective function and local inequality constraints; agents make decisions online and cooperate to minimize the sum of the time-varying objectives while satisfying the global coupled constraints. To solve the problem, we propose an algorithm based on the primal-dual approach combined with constraint-tracking. Under appropriate assumptions that the objective and constraint functions are convex, their gradients are uniformly bounded, and the path length of the optimal solution sequence grows sublinearly, we analyze theoretical properties of the proposed algorithm and prove that both the dynamic regret and the constraint violation are sublinear with time horizon T. Finally, we evaluate the proposed algorithm on a time-varying economic dispatch problem in power systems using both synthetic data and Australian Energy Market data. The results demonstrate that the proposed algorithm performs effectively in terms of tracking performance, constraint satisfaction, and adaptation to time-varying disturbances, thereby providing a practical and theoretically well-supported solution for real-time distributed economic dispatch.
February 9, 2026 at 1:38 AM
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arXiv📈🤖
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach
By
arXiv:2210.15829v3 Announce Type: replace Abstract: We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with multiple exogenous covariates and a potentially endogenous treatment variable. Our approach integrates a Robinson transformation to handle the nonparametric component, the Smooth Minimum Distance (SMD) method to leverage conditional mean independence restrictions, and a Neyman-Orthogonalized first-order condition (FOC). By employing regularized model selection techniques like the Lasso method, our estimator accommodates numerous covariates while exhibiting reduced bias, consistency, and asymptotic normality. Simulations demonstrate its robust performance with diverse instrument sets compared to traditional GMM-type estimators. Applying this method to estimate Medicaid's heterogeneous treatment effects from the Oregon Health Insurance Experiment reveals more robust and reliable results than conventional GMM approaches.
February 8, 2026 at 10:08 PM
paperposterbot.bsky.social
arXiv📈🤖
Flexible Regularized Estimation in High-Dimensional Mixed Membership Models
By
Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.
February 8, 2026 at 7:11 PM
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arXiv📈🤖
Synthetic Regressing Control
By Zhu
Estimating weights in the synthetic control method, typically resulting in sparse weights where only a few control units have non-zero weights, involves an optimization procedure that selects and combines control units to closely match the treated unit. However, it is not uncommon for the linear combination of pre-treatment period outcomes for the control units, using nonnegative weights with the constraint that their sum equals one, to inadequately approximate the pre-treatment outcomes for the treated unit. To address the issue, this paper proposes a simple and effective method called Synthetic Regressing Control (SRC). The SRC method begins by performing the univariate linear regression to appropriately align the pre-treatment periods of the control units with the treated unit. Subsequently, a SRC estimator is obtained by synthesizing the regressed controls. To determine the weights in the synthesis procedure, we propose an approach that utilizes a criterion of an unbiased risk estimator. Theoretically, we show that the synthesis way is asymptotically optimal in the sense of achieving the minimum loss of the infeasible best possible synthetic estimator. Extensive numerical experiments highlight the advantages of the SRC method.
February 8, 2026 at 4:09 PM
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arXiv📈🤖
Generalized Optimal Transport
By Voronin
Many causal and structural parameters in economics can be identified and estimated by computing the value of an optimization program over all distributions consistent with the model and the data. Existing tools apply when the data is discrete, or when only disjoint marginals of the distribution are identified, which is restrictive in many applications. We develop a general framework that yields sharp bounds on a linear functional of the unknown true distribution under i) an arbitrary collection of identified joint subdistributions and ii) structural conditions, such as (conditional) independence. We encode the identification restrictions as a continuous collection of moments of characteristic kernels, and use duality and approximation theory to rewrite the infinite-dimensional program over Borel measures as a finite-dimensional program that is simple to compute. Our approach yields a consistent estimator that is $\sqrt{n}$-uniformly valid for the sharp bounds. In the special case of empirical optimal transport with Lipschitz cost, where the minimax rate is $n^{2/d}$, our method yields a uniformly consistent estimator with an asymmetric rate, converging at $\sqrt{n}$ uniformly from one side.
February 8, 2026 at 4:01 AM
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arXiv📈🤖
Modifying Survival Models To Accommodate Thresholding Behavior
By
arXiv:2212.07602v2 Announce Type: replace Abstract: Survival models capture the relationship between an accumulating hazard and the occurrence of a singular event stimulated by that accumulation. When the model for the hazard is sufficiently flexible survival models can accommodate a wide range of behaviors. If the hazard model is less flexible, for example when it is constrained by an external physical process, then the resulting survival model can be much too rigid. In this paper I introduce a modified survival model that generalizes the relationship between accumulating hazard and event occurrence with particular emphasis on capturing thresholding behavior. Finally I demonstrate the utility of this approach on a physiological application.
February 8, 2026 at 1:37 AM
paperposterbot.bsky.social
arXiv📈🤖
Using Forests in Multivariate Regression Discontinuity Designs
By
arXiv:2303.11721v2 Announce Type: replace Abstract: We discuss estimation and inference of conditional treatment effects in regression discontinuity designs with multiple scores. Aside from the commonly used local linear regression approach and a minimax-optimal estimator recently proposed by Imbens and Wager (2019), we consider two estimators based on random forests -- honest regression forests and local linear forests -- whose construction resembles that of standard local regressions, with theoretical validity following from results in Wager and Athey (2018) and Friedberg et al. (2020). We design a systematic Monte Carlo study with data generating processes built both from functional forms that we specify and from Wasserstein Generative Adversarial Networks that can closely mimic the observed data. We find that no single estimator dominates across all simulations: (i) local linear regressions perform well in univariate settings, but can undercover when multivariate scores are transformed into a univariate score -- which is commonly done in practice -- possibly due to the "zero-density" issue of the collapsed univariate score at the transformed cutoff; (ii) good performance of the minimax-optimal estimator depends on accurate estimation of a nuisance parameter and its current implementation only accepts up to two scores; (iii) forest-based estimators are not designed for estimation at boundary points and can suffer from bias in finite sample, but their flexibility in modeling multivariate scores opens the door to a wide range of empirical applications in multivariate regression discontinuity designs.
February 7, 2026 at 10:08 PM
paperposterbot.bsky.social
arXiv📈🤖
A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes
By Baghfalaki, Hashemi, Tzourio et al
Background: In clinical and epidemiological research, the integration of longitudinal measurements and time-to-event outcomes is vital for understanding relationships and improving risk prediction. However, as the number of longitudinal markers increases, joint model estimation becomes more complex, leading to long computation times and convergence issues. This study introduces a novel two-stage Bayesian approach for variable selection in joint models, illustrated through a practical application. Methods: Our approach conceptualizes the analysis in two stages. In the first stage, we estimate one-marker joint models for each longitudinal marker related to the event, allowing for bias reduction from informative dropouts through individual marker trajectory predictions. The second stage employs a proportional hazard model that incorporates expected current values of all markers as time-dependent covariates. We explore continuous and Dirac spike-and-slab priors for variable selection, utilizing Markov chain Monte Carlo (MCMC) techniques. Results: The proposed method addresses the challenges of parameter estimation and risk prediction with numerous longitudinal markers, demonstrating robust performance through simulation studies. We further validate our approach by predicting dementia risk using the Three-City (3C) dataset, a longitudinal cohort study from France. Conclusions: This two-stage Bayesian method offers an efficient process for variable selection in joint modeling, enhancing risk prediction capabilities in longitudinal studies. The accompanying R package VSJM, which is freely available at https://github.com/tbaghfalaki/VSJM, facilitates implementation, making this approach accessible for diverse clinical applications.
February 7, 2026 at 7:10 PM
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arXiv📈🤖
Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review
By
We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series. We consider two cases: (1) estimation when no dynamic model for the factors is specified \citep{baili12,baili16}; (2) estimation based on the Kalman smoother and the Expectation Maximization algorithm thus allowing to model explicitly the factor dynamics \citep{DGRqml,BLqml}. Our interest is in approximate factor models, i.e., when we allow for the idiosyncratic components to be mildly cross-sectionally, as well as serially, correlated. Although such setting apparently makes estimation harder, we show, in fact, that factor models do not suffer of the {\it curse of dimensionality} problem, but instead they enjoy a {\it blessing of dimensionality} property. In particular, given an approximate factor structure, if the cross-sectional dimension of the data, $N$, grows to infinity, we show that: (i) identification of the model is still possible, (ii) the mis-specification error due to the use of an exact factor model log-likelihood vanishes. Moreover, if we let also the sample size, $T$, grow to infinity, we can also consistently estimate all parameters of the model and make inference. The same is true for estimation of the latent factors which can be carried out by weighted least-squares, linear projection, or Kalman filtering/smoothing. We also compare the approaches presented with: Principal Component analysis and the classical, fixed $N$, exact Maximum Likelihood approach. We conclude with a discussion on efficiency of the considered estimators.
February 7, 2026 at 4:08 PM
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arXiv📈🤖
Kriging for large datasets via penalized neighbor selection
By Cuevas-Pacheco, Acosta
Kriging is a fundamental tool for spatial prediction, but its computational complexity of $O(N^3)$ becomes prohibitive for large datasets. While local kriging using $K$-nearest neighbors addresses this issue, the selection of $K$ typically relies on ad-hoc criteria that fail to account for spatial correlation structure. We propose a penalized kriging framework that incorporates LASSO-type penalties directly into the kriging equations to achieve automatic, data-driven neighbor selection. We further extend this to adaptive LASSO, using data-driven penalty weights that account for the spatial correlation structure. Our method determines which observations contribute non-zero weights through $\ell_1$ regularization, with the penalty parameter selected via a novel criterion based on effective sample size that balances prediction accuracy against information redundancy. Numerical experiments demonstrate that penalized kriging automatically adapts neighborhood structure to the underlying spatial correlation, selecting fewer neighbors for smoother processes and more for highly variable fields, while maintaining prediction accuracy comparable to global kriging at substantially reduced computational cost.
February 7, 2026 at 3:47 AM
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arXiv📈🤖
Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health
By
arXiv:2404.13442v1 Announce Type: new Abstract: Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of emissions control technologies on power plants. However, assessing the health impacts of these measures, particularly over longer periods of time, is complicated by several factors. First, the units that potentially receive the intervention (power plants) are disjoint from those on which outcomes are measured (communities), and second, pollution emitted from power plants disperses and affects geographically far-reaching areas. This creates a methodological challenge known as bipartite network interference (BNI). To our knowledge, no methods have been developed for conducting quasi-experimental studies with panel data in the BNI setting. In this study, motivated by the need for robust estimates of the total health impacts of power plant emissions control technologies in recent decades, we introduce a novel causal inference framework for difference-in-differences analysis under BNI with staggered treatment adoption. We explain the unique methodological challenges that arise in this setting and propose a solution via a data reconfiguration and mapping strategy. The proposed approach is advantageous because analysis is conducted at the intervention unit level, avoiding the need to arbitrarily define treatment status at the outcome unit level, but it permits interpretation of results at the more policy-relevant outcome unit level. Using this interference-aware approach, we investigate the impacts of installation of flue gas desulfurization scrubbers on coal-fired power plants on coronary heart disease hospitalizations among older Americans over the period 2003-2014, finding an overall beneficial effect in mitigating such disease outcomes.
February 7, 2026 at 1:36 AM