ArXiv Paperboy (Stat.ME+Econ.EM)

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Benchmarking Observational Studies with Experimental Data under Right-Censoring () arXiv:2402.15137v1 Announce Type: new
Abstract: Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmark

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DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data (Ghosh, Feng, Bian et al) Determining causal effects of interventions onto outcomes from real-world, observational (non-ra

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Bayesian Variable Selection via Hierarchical Gaussian Process Model in Computer Experiments () arXiv:2406.11306v1 Announce Type: new
Abstract: Identifying the active factors that have significant impacts on the output of the complex system is an important but challenging variable selecti

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Nonparametric inference of higher order interaction patterns in networks () arXiv:2403.15635v1 Announce Type: cross
Abstract: We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs.The method is

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DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data (Ghosh, Feng, Bian et al) Determining causal effects of interventions onto outcomes from real-world, observational (non-ra

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Semi-Implicit Approaches for Large-Scale Bayesian Spatial Interpolation (Epidemiology, Health, University) et al) Spatial statistics often rely on Gaussian processes (GPs) to capture dependencies across locations. However, their computational cost increases rapidly with the number of loca

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Policy Learning with Abstention (Sawarni, Jin, Whitehouse et al) Policy learning algorithms are widely used in areas such as personalized medicine and advertising to develop individualized treatment regimes. However, most methods force a decision even when predictions are uncertain, which

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Knowledge Distillation of Uncertainty using Deep Latent Factor Model (Park, Lee, Shin et al) Deep ensembles deliver state-of-the-art, reliable uncertainty quantification, but their heavy computational and memory requirements hinder their practical deployments to real applications such as

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Simulation-Guided Planning of a Target Trial Emulated Cluster Randomized Trial for Mass Small-Quantity Lipid Nutrient Supplementation Combined with Expanded Program on Immunization in Rural Niger (Metcalfe, Dyrkton, Yan et al) While target trial emulation (TTE) is increasingly used to imp

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A Class of Markovian Self-Reinforcing Processes with Power-Law Distributions (Bulanchuk, Koay, Romani) Solar flares, email exchanges, and many natural or social systems exhibit bursty dynamics, with periods of intense activity separated by long inactivity. These patterns often follow powe

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Network Contagion Dynamics in European Banking: A Navier-Stokes Framework for Systemic Risk Assessment (Kikuchi) This paper develops a continuous functional framework for analyzing contagion dynamics in financial networks, extending the Navier-Stokes-based approach to network-structured s

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Robust Rank Estimation for Noisy Matrices (Roy, Ghosh, Basu) Estimating the true rank of a noisy data matrix is a fundamental problem underlying techniques such as principal component analysis, matrix completion, etc. Existing rank estimation criteria, including information-based and cros

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Living Synthetic Benchmarks: A Neutral and Cumulative Framework for Simulation Studies (Barto\v{s}, Pawel, Siepe) Simulation studies are widely used to evaluate statistical methods. However, new methods are often introduced and evaluated using data-generating mechanisms (DGMs) devised by

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Using did_multiplegt_dyn to Estimate Event-Study Effects in Complex Designs: Overview, and Four Examples Based on Real Datasets (Chaisemartin, Ciccia, Knau et al) The command did_multiplegt_dyn can be used to estimate event-study effects in complex designs with a potentially non-binary an

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Hierarchical Overlapping Group Lasso for GMANOVA Model (Ohishi, Nagai, Oda et al) This paper deals with the GMANOVA model with a matrix of polynomial basis functions as a within-individual design matrix. The model involves two model selection problems: the selection of explanatory variabl

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A New Targeted-Federated Learning Framework for Estimating Heterogeneity of Treatment Effects: A Robust Framework with Applications in Aging Cohorts (Zhao, Falvey, Shi et al) Analyzing data from multiple sources offers valuable opportunities to improve the estimation efficiency of causal

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No Intelligence Without Statistics: The Invisible Backbone of Artificial Intelligence (Fokou\'e) The rapid ascent of artificial intelligence (AI) is often portrayed as a revolution born from computer science and engineering. This narrative, however, obscures a fundamental truth: the theor

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Inverse-intensity weighted generalized estimating equations with irregularly measured longitudinal data and informative dropout (Stefan, Pullenayegum) Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects

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An Empirical Framework for Discrete Games with Costly Information Acquisition (Jeong) This paper develops a novel econometric framework for static discrete choice games with costly information acquisition. In traditional discrete games, players are assumed to perfectly know their own payo

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Efficient scenario analysis in real-time Bayesian election forecasting via sequential meta-posterior sampling (Han, Gelman, Vehtari) Bayesian aggregation lets election forecasters combine diverse sources of information, such as state polls and economic and political indicators: as in our

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Spatially Regularized Gaussian Mixtures for Clustering Spatial Transcriptomic Data (Sottosanti, Risso, Denti) Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investi

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Estimation of causal dose-response functions under data fusion (Lim, Luedtke) Estimating the causal dose-response function is challenging, particularly when data from a single source are insufficient to estimate responses precisely across all exposure levels. To overcome this limitation,

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Centered MA Dirichlet ARMA for Financial Compositions: Theory & Empirical Evidence (Katz) Observation-driven Dirichlet models for compositional time series often use the additive log-ratio (ALR) link and include a moving-average (MA) term built from ALR residuals. In the standard B--DARMA

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Phylogenetic latent space models for network data (Pavone, Durante, Ryder) Latent space models for network data characterize each node through a vector of latent features whose pairwise similarities define the edge probabilities among pairs of nodes. Although this formulation has led to s

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Empirical Bayes inference in sparse high-dimensional generalized linear models () arXiv:2303.07854v2 Announce Type: replace-cross
Abstract: High-dimensional linear models have been widely studied, but the developments in high-dimensional generalized linear models, or GLMs, have been slow

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Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators () Reinforcement learning can learn amortised design policies for designing sequences of experiments. However, current amortised methods rely on estimators of expected in

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A model-free subdata selection method for classification () arXiv:2404.19127v1 Announce Type: new
Abstract: Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata s

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Comparison of Simulation-Guided Design to Closed-Form Power Calculations in Planning a Cluster Randomized Trial with Covariate-Constrained Randomization: A Case Study in Rural Chad (Park, Metcalfe, Dyrkton et al) Current practices for designing cluster-randomized trials (cRCTs) typically

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A Frequentist Statistical Introduction to Variational Inference, Autoencoders, and Diffusion Models (Chen) While Variational Inference (VI) is central to modern generative models like Variational Autoencoders (VAEs) and Denoising Diffusion Models (DDMs), its pedagogical treatment is split

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Distributional regression for seasonal data: an application to river flows (Perreault, Pesenti, Shahzad) Risk assessment in casualty insurance, such as flood risk, traditionally relies on extreme-value methods that emphasizes rare events. These approaches are well-suited for characterizin