{"total":35,"items":[{"citing_arxiv_id":"2606.30375","ref_index":40,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multiple testing with the horseshoe","primary_cat":"math.ST","submitted_at":"2026-06-29T14:36:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.30229","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficiency of Valid Inferential Models: Choquet-risk Optimal Possibility Measures, and Direct Comparisons","primary_cat":"math.ST","submitted_at":"2026-06-29T12:42:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Choquet risk ranks valid possibilistic inferential models by linking their efficiency to expected performance of induced confidence sets under concentration penalties.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28871","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification","primary_cat":"stat.ML","submitted_at":"2026-06-27T11:29:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28738","ref_index":109,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data","primary_cat":"stat.ME","submitted_at":"2026-06-27T05:21:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ARC framework models high-dimensional compositional data with exact zeros by treating compositions as directions of latent vectors with an explicit active-set process on the hypersphere.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.27957","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From streaks to synergies: A multi-scale analysis of performance and scoring in the NBA","primary_cat":"physics.soc-ph","submitted_at":"2026-06-26T10:59:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Multi-scale empirical analysis of NBA scoring streaks, synergies, and performance using 2020-2025 play-by-play data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.26804","ref_index":12,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Structured Secant Methods to Select Smoothing Parameters For General Smooth Models","primary_cat":"stat.ME","submitted_at":"2026-06-25T09:42:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A structured secant quasi-Newton method (qEFS) for smoothing parameter selection in general smooth models that approximates the Hessian and is easier to implement than exact second-order methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18949","ref_index":19,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Feature Screening for High-Dimensional Structural Break Predictive Regression","primary_cat":"stat.ME","submitted_at":"2026-06-17T11:32:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Develops SICS and RCRS screening methods for consistent selection of sparse active predictors and change points in high-dimensional structural break predictive regressions that may involve stationary or cointegrated series.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12654","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Computationally tractable robust differentially private mean estimation","primary_cat":"stat.ME","submitted_at":"2026-06-10T20:25:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The balloon mean is a computationally tractable robust differentially private mean estimator with theoretical guarantees under heavy-tailed contaminated elliptical models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10767","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Two-Sample Homogeneity Test via Entropic Optimal Transport","primary_cat":"stat.ME","submitted_at":"2026-06-09T12:21:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes and analyzes a homogeneity test using squared L2 distance of empirical EOT maps to uniform-on-ball reference, with FCLT, Gaussian quadratic null limit, consistency, local power, and weighted multiplier bootstrap.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07677","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Disentangling Latent Risk Pathways via Bayesian Hypergraph Inference","primary_cat":"stat.ML","submitted_at":"2026-06-04T19:56:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A Bayesian hypergraph inference method models EHR multi-disease risk by letting risk factors modulate latent hyperedges (disease subsets) with repulsion priors and structured variational inference for uncertainty and scalability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04673","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Improving Longitudinal Targeted Maximum Likelihood Estimation in Target Trial Emulation using Joint Calibrated Weights","primary_cat":"stat.ME","submitted_at":"2026-06-03T09:55:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint calibrated LTMLE integrates LTMLE with joint calibrated weights to improve finite-sample efficiency and robustness to misspecification for per-protocol effect estimation in target trial emulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04237","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Constrained Weighted Bayesian Bootstrap","primary_cat":"stat.ME","submitted_at":"2026-06-02T21:37:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Constrained weighted Bayesian bootstrap extends weighted Bayesian bootstrap to constrained posteriors with asymptotics matching restricted MLE and is demonstrated on option pricing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03355","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"APIC: Amortized Physics-Informed Calibration using Neural Processes","primary_cat":"cs.LG","submitted_at":"2026-06-02T09:04:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"APIC applies Neural Processes in a two-branch latent model to amortize Kennedy-O'Hagan-style calibration, separating instance-specific parameters from shared structural discrepancies for fast inference on new realizations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31529","ref_index":12,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence","primary_cat":"cs.CV","submitted_at":"2026-05-29T16:43:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SVI-Bench provides 35K hours of sports video with 9 tasks across four cognitive levels, revealing models drop from ~74% on action QA to 5% on agentic evidence integration.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00181","ref_index":136,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Infinite-Dimensional Spherical Kernel ridge Regression","primary_cat":"stat.ME","submitted_at":"2026-05-29T14:46:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An intrinsic spherical kernel ridge regression framework is introduced for non-linear responses on spheres, reducing infinite-dimensional estimation to finite via the representer theorem with convergence rates shown.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.30492","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Shrinkage-Constrained Functional Calibration for Complex Computer Models","primary_cat":"stat.ME","submitted_at":"2026-05-28T19:16:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"IBFU extends KOH calibration by representing parameter corrections as GPs with shrinkage priors that nest the fixed-parameter case while permitting controlled input-dependent variation when supported by data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27137","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bernstein-von Mises Theorem for Sparse Generalized Linear Model","primary_cat":"math.ST","submitted_at":"2026-05-26T15:06:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Proves oracle Bernstein-von Mises theorem for fractional posterior under supportwise likelihood assumptions in sparse GLMs with spike-and-slab priors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25496","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Estimation of Directed Acyclic Graphs by Frequentist Model Averaging","primary_cat":"stat.ME","submitted_at":"2026-05-25T06:57:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A model averaging estimator for DAGs in Gaussian graphical models achieves asymptotic optimality, weight consistency, parameter consistency, and consistency even under complete misspecification of all candidate graphs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23082","ref_index":43,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis","primary_cat":"stat.ML","submitted_at":"2026-05-21T22:30:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KAPLAN-HR applies B-spline KANs to nonparametric hazard estimation in survival analysis, recovering GAMs in the single-layer case, capturing interactions via deeper layers, with convergence rates independent of covariate dimension for KAN-representable targets, and competitive performance on six cli","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16593","ref_index":268,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients","primary_cat":"stat.AP","submitted_at":"2026-05-15T19:56:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10088","ref_index":2,"ref_count":4,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Sample size and power calculations for causal inference with time-to-event outcomes","primary_cat":"stat.ME","submitted_at":"2026-05-11T07:07:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.","context_count":2,"top_context_role":"background","top_context_polarity":"background","context_text":"model misspecification: Model(1) is rarely the data-generating process, so variance estimators based solely on the empirical partial scoreψ∗ i (τ)are not applicable (Andersen and Gill, 1982). Lin and Wei (1989) proposed a sandwich variance estimator robust to model misspecification for the unweighted Cox model. Binder (1992) extended it to survey settings with known sampling weight, taking the form[Pn i=1 ψ∗ i (τ)]−2[Pn i=1 η∗ i (τ)2], whereη ∗ i (τ)is the weighted empirical influence function. Because power analysis is conducted at the design stage prior to data collection, our central task is to approximate V using only a few expected summary quantities. This requires an analytical expression of V, which would reveal under what population-level assumptionsV reduces to a tractable function of these"},{"citing_arxiv_id":"2605.08995","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Semiparametric Elliptical Mixture Clustering for High-Dimensional Data","primary_cat":"stat.ME","submitted_at":"2026-05-09T15:25:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A semiparametric framework clusters high-dimensional elliptical data with heavy tails via cluster-specific centers, a common unknown radial generator, and a shared sparse precision matrix, with GEM algorithm and high-dimensional consistency guarantees.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"denotes a squared Mahalanobis radius. 2.Model and Method 2.1.Semiparametric elliptical mixture with fixed K.Let Z∈ { 1, . . . , K} be a latent class label with P(Z=k) =π ∗ k, KX k=1 π∗ k = 1, π ∗ k >0. Conditional onZ=k, the observationX∈R p has density f ∗ k(x) =|Ω ∗|1/2g∗(δ∗ k(x)), δ ∗ k(x) = (x−µ ∗ k)⊤Ω∗(x−µ ∗ k), whereΩ ∗ ≻ 0 is a common precision-shape matrix, and g∗ : [0,∞ ) → [0,∞ ) is an unknown common radial generator, and satisfying πp/2 Γ(p/2) R ∞ 0 up/2−1g∗(u) du = 1. The observed marginal density is therefore f ∗(x) = KX k=1 π∗ k|Ω∗|1/2g∗(δ∗ k(x)). The parameter is z∗ = (π∗,µ ∗ 1, . . . ,µ∗ K,Ω ∗, g∗). Because the generator is unknown, (Ω ∗, g∗) is identifiable only up to scale. Throughout the paper we impose the trace normalization (2.1) tr{(Ω ∗)−1}=p. Write ℓ∗(u) = logg ∗(u), ω ∗(u) =− d du ℓ∗(u). Since the determinant term is common to every component, the oracle Bayes rule is G∗(x) = argmax 1≤k≤K n logπ ∗ k +ℓ ∗(δ∗ k(x)) o . SEMIPARAMETRIC ELLIPTICAL MIXTURE CLUSTERING 5 Under the 0-1 loss, the corresponding Bayes risk is denoted byR(G ∗) =P{G ∗(X)̸=Z}. 2.2.Oracle EM map.The class labels are latent, so an EM-type construction is the natural starting point for model-based clustering under the mixture model. For a given candidate parameter, the E-step replaces the unobserved labels by posterior responsibilities, and the M-step updates the model blocks conditionally on those responsibilities. In the present semiparametric elliptical setting, however, the generator g is unknown and the common precision matrix must be regularized when p is large, so a literal closed-form EM maximization is neither available nor numerically stable. We therefore define an oracle update map that preserves the exact posterior-weight structure of EM and couples it with a robust high-dimensional common-shape routine. The oracle map below follows the same block order as the practical algorithm. For a generic candidate parameter z= (π,µ 1, . . . ,µK,Ω, g), let δk(x;z) "},{"citing_arxiv_id":"2605.08404","ref_index":126,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models","primary_cat":"cs.CL","submitted_at":"2026-05-08T19:10:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Large vision-language models applied to multi-scale remote sensing imagery can generate recommendations on built environment design, constructability, land use, and risks for smart city decision-making.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07834","ref_index":140,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GenAI Powered Dynamic Causal Inference with Unstructured Data","primary_cat":"stat.ME","submitted_at":"2026-05-08T15:03:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A GenAI-based method extracts representations from unstructured data and uses a neural network to fit marginal structural models that recover causal effects of treatment feature sequences including their positions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00056","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution","primary_cat":"cs.LG","submitted_at":"2026-04-29T21:40:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"To mitigate this sensitivity, the mean absolute error (MAE) was also employed (see Eq. (25)): MAE = 1 n nX i=1 |yi − ˆyi|.(25) Unlike RMSE, MAE weights all deviations equally, providing a robust alternative when extreme residuals occur. The median absolute error (MedAE) in Eq. (25) further enhances robust- ness against outliers by considering the median rather than the mean: MedAE = median (|yi − ˆyi|) .(26) Finally, the maximum error (MaxError) quantifies the single worst-case deviation as expressed in Eq. (27): MaxError = max i |yi − ˆyi|,(27) which is particularly relevant in groundwater risk mapping where isolated but extreme mispredictions can mask localised contamination hotspots. 5.2 Goodness-of-fit and information-theoretic criteria The explanatory power of the models was assessed through the coefficient of"},{"citing_arxiv_id":"2604.22015","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data","primary_cat":"stat.ME","submitted_at":"2026-04-23T19:08:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HPPCA is a hierarchical extension of PPCA that uses Gaussian processes to model within-subject dynamics in longitudinal data, outperforming standard PPCA and functional PCA in imputation under missingness and misspecification.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20285","ref_index":214,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Time-dependent structural equation modeling of fans' football fever using activity tracking data during the 2025 DFB Cup final","primary_cat":"stat.AP","submitted_at":"2026-04-22T07:31:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Football fever in spectators follows a V-shaped time course captured as a latent process from heart rate and stress data via time-dependent structural equation modeling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04964","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bayesian Global-Local Shrinkage with Univariate Guidance for Ultra-High-Dimensional Regression","primary_cat":"stat.ME","submitted_at":"2026-04-03T22:41:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"Updates of(λ j), τ, c2, η.The remaining parameters enter the model through the nonlinear shrink- age factor ˜κ2 j and are updated via univariate slice sampling (Neal, 2003) on suitable log-transformed scales. In particular, their conditional densities arise from the dependence of ˜κ 2 j on (λj, τ, c2, η). We updateλ j,τ, andc 2 on log-scales, andηon [0,∞), using their respective conditionals. Details are provided in Supplementary Material S2. 3.2 Active-set MCMC approximation for ultra-high dimensions For moderate to high-dimensional settings (e.g.,p≲10 4), the full MCMC sampler provides exact posterior inference. In ultra-high-dimensional regimes (p≫10 4), however, updating all local shrink- age parameters{λ j}p j=1 at each iteration becomes computationally prohibitive. To address this, we introduce an active-set MCMC approximation that restricts local updates to a subset of coordinates likely to be influential. This exploits a key property of global-local shrinkage priors: most coeffi- cients are strongly shrunk toward zero, so updating their associated local scales has negligible impact on posterior inference. Related ideas have been explored in scalable Bayesian regression (Johndrow et al., 2020). At each MCMC iteration, we construct an active setA n ⊂ {1, . . . , p}, which may vary across iterations, consisting of coordinates deemed potentially relevant based on the current state of the chain. A coordinatejis included inA n if it satisfies either of the following: 1. it belongs to a fixed-size subset of predictors with the largest marginal guidance values|˜z ∗ j | (theguidance budget), or 2. its current coefficient magnitude exceeds a threshold,|β j|> t n. The active set may additionally be capped to include only the largest coefficients by magnitude to control computational cost. GivenA n, local shrinkage parameters are updated only for indices in An, while coefficients are updated globally. 10 The MCMC updates then proceed as follows. The regression"},{"citing_arxiv_id":"2601.23030","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural Backward Filtering Forward Guiding","primary_cat":"stat.ML","submitted_at":"2026-01-30T14:39:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NBFFG combines a closed-form backward filter from a linear-Gaussian proxy process with a learned neural residual to enable efficient variational inference and unbiased pathwise subsampling for nonlinear diffusions on trees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.24087","ref_index":42,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A penalized distributed lag non-linear Lee-Carter framework for regional weekly mortality forecasting","primary_cat":"stat.AP","submitted_at":"2025-09-28T21:46:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.03512","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bayesian Multivariate Sparse Functional Principal Components Analysis","primary_cat":"stat.ME","submitted_at":"2025-09-03T17:52:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.09254","ref_index":67,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Moving towards informative and actionable social media research","primary_cat":"cs.SI","submitted_at":"2025-05-14T10:02:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Social media research yields inconclusive causal findings due to system complexity, and progress requires mechanistic explanations that integrate observational and experimental approaches while recognizing their shared limitations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.18250","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Interplay between pitch control and top speed in soccer: The stamina factor","primary_cat":"physics.soc-ph","submitted_at":"2025-04-25T10:50:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Soccer pitch control model with heterogeneous player top speeds finds role-dependent positive correlations and a logarithmic effect from a stamina factor that adjusts speed.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.04280","ref_index":84,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects","primary_cat":"math.PR","submitted_at":"2025-02-06T18:27:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2501.18250","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural CSI Compression Fine-Tuning: Taming the Communication Cost of Model Updates","primary_cat":"cs.IT","submitted_at":"2025-01-30T10:31:34+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Cost-aware full-model fine-tuning with joint entropy coding and structured sparsity prior improves rate-distortion performance of neural CSI compression under distribution shifts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}