{"total":18,"items":[{"citing_arxiv_id":"2606.31970","ref_index":66,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Payment Process Estimation in Aggregated Insurance Models","primary_cat":"stat.ME","submitted_at":"2026-06-30T17:11:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Establishes strong consistency and weak convergence for inverse-probability-weighted estimators of state-specific cumulative payment processes in a sojourn-payment model for aggregated multi-state systems under left-truncation and right-censoring.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.29519","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Anti-Collapse Dynamics and the Emergence of Multi-Time-Scale Learning in Recurrent Neural Networks","primary_cat":"cs.LG","submitted_at":"2026-06-28T17:30:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RNNs can sustain power-law forgetting and multi-time-scale learning when heavy-tailed fluctuations in SGD balance the collapse tendency toward short time scales, governed by a spectral exponent β.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28540","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Choosing the threshold in extreme value analysis","primary_cat":"stat.ME","submitted_at":"2026-06-26T18:48:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23575","ref_index":81,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression","primary_cat":"cs.LG","submitted_at":"2026-06-22T16:41:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20427","ref_index":99,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Private Rate-Double-Robust Inference","primary_cat":"math.ST","submitted_at":"2026-06-18T16:08:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The term pnPn ˜χ PV X ⇝ N (0, PV X ˜χ2) by the standard central limit theorem. The second term in ( 96) is called the empirical process term and is vanishing as oPV X (1) under consistent estimators ˆη and stochastic boundedness conditions. Assumption 4 (Consistent Estimators). It holds that kˆr rkL2(PV X ) = oPV X (1) , (98) ˆγV(c) γV(c) = oPV X (1) , (99) ˆpV2(c) pV2(c) = oPV X (1) . (100) 62 Further , it either holds that km µX k∞ = O (1) , (101) kµX ˆµX k∞ = oPV X (1) , (102) or that km ˆµX k∞ = OPV X (1) , (103) kµX ˆµX kL2(PV X ) = oPV X (1) , (104) PV X \b (V1, X) 2 V1 \u0002 X : jr(V1, X)j > ¯R \u0001 = 0 (105) for some constant ¯R < 1, where (105) may be replaced by krk∞ < 1. Lemma 9 (Vanishing Empirical Process T erm) ."},{"citing_arxiv_id":"2606.12317","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ShrinkageTrees: An R Package for Bayesian Tree Ensembles for Survival Analysis and Causal Inference","primary_cat":"stat.ME","submitted_at":"2026-06-10T16:52:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11183","ref_index":48,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds","primary_cat":"math.ST","submitted_at":"2026-06-09T17:58:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11267","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A prior-free blind detection of information leakage from model predictions","primary_cat":"cs.LG","submitted_at":"2026-06-09T05:13:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A framework proves that broad recalibrated leakage is undetectable from predictions alone without an external discrimination ceiling, while near-label leaks produce a detectable unit-purity signature yielding a prior-free test.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31189","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction","primary_cat":"cs.LG","submitted_at":"2026-05-29T11:56:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"FlagGAM builds sparse univariate rule bases from features and feeds them into a restricted additive model, achieving competitive accuracy with superior robustness to missingness and noise on tabular benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27619","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Supervised Distributional Reduction via Optimal Transport and Dependence Maximization","primary_cat":"cs.LG","submitted_at":"2026-05-26T19:38:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SDR augments the Fused Gromov-Wasserstein objective with an explicit dependence term to learn target-aware distributional representations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19989","ref_index":259,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Error Bounds for Importance Sampling with Estimated Proposal Distributions","primary_cat":"math.ST","submitted_at":"2026-05-19T15:27:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00060","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting","primary_cat":"q-fin.TR","submitted_at":"2026-05-19T14:30:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Cost-aware execution filters enable selected machine learning strategies, particularly long-only XGBoost, to achieve over 65% annualized returns and Sharpe ratios above 1 in hourly BTC trading despite 10bp costs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19164","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Spatial Cram'{e}r--von Mises Test of Independence under $\\beta$-Mixing: Asymptotic Theory and Python Implementation","primary_cat":"stat.ME","submitted_at":"2026-05-18T22:32:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Derives the asymptotic distribution of the spatial Cramér-von Mises independence statistic under β-mixing on R² and implements it in Python with eigenvalue-based critical values.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07920","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Primitive Sequences for Probability Measures on Compact Intervals","primary_cat":"math.PR","submitted_at":"2026-05-08T15:55:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Primitive sequences obtained from iterated antiderivatives of the CDF are homeomorphic to probability measures on compact intervals, equivalent to factorial-rescaled moments of the reflected variable, and yield sharp bounds on functionals when the first m terms are fixed.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"r:= 1!c1, . . . , m!cm \u0001⊤ ∈[0,∞) m, s(x;x 0) := 1{x1 ≤x 0}, . . . ,1{xm+1 ≤x 0} \u0001⊤ ∈ {0,1} m+1, and V(x) :=   (b−x 1)1 · · ·(b−x 1)m ... ... ... (b−x m+1)1 · · ·(b−x m+1)m   ∈R (m+1)×m. The sharp lower bound for the left limit admits a similar representation: F m(x0;c 1:m) = min x∈[a,b]m+1 w∈[0,1]m+1 s′(x;x 0)⊤w subject toV(x) ⊤w=randw ⊤1= 1, (28) 23 where s′(x;x 0) := 1{x1 < x 0}, . . . ,1{xm+1 < x 0} \u0001⊤ ∈ {0,1} m+1, The upper and lower bounds in Corollary 7 are more conveniently computed through dual formulations in terms of polynomials, as explained in the context of the generalized duality principle in Chapter IX of Krein and Nudel'man (1977). This approach reformulates the search over probability"},{"citing_arxiv_id":"2605.07671","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting","primary_cat":"cs.GT","submitted_at":"2026-05-08T12:42:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"by studying what happens when the intermediary canmisreport(not just withhold), disciplined by a proper scoring mechanism. 1.6 Related Literature Proper scoring rules and elicitation theory.The characterization of strictly proper scoring rules originates with de Finetti [17], Brier [13], McCarthy [44], and Savage [55]. The definitive modern treatment is Gneiting and Raftery [27]. Schervish [56] provides the general characterization linking properness to convex functions. Lambert et al. [37] uses conjugate duality to characterize elicitable properties of probability distributions. The connection between proper scoring rules and convex analysis is further developed by Abernethy and Frongillo [1] and the information-elicitation litera- ture."},{"citing_arxiv_id":"2509.02829","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem","primary_cat":"math.PR","submitted_at":"2025-09-02T20:51:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An iterated I-projection procedure solves the generalized minimum information checkerboard copula problem with convergence guarantees and numerical tests up to dimension four.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.01781","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Proper scoring rules for estimation and forecast evaluation","primary_cat":"math.ST","submitted_at":"2025-04-02T14:46:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A review summarizing mathematical foundations, characterization results, families of proper scoring rules, and their roles in statistics and machine learning for estimation and forecast evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.17773","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective","primary_cat":"stat.ME","submitted_at":"2025-02-25T02:07:29+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}