{"total":18,"items":[{"citing_arxiv_id":"2606.21551","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Reformulation Invariance and the Axiomatic Foundations of Inference","primary_cat":"math.ST","submitted_at":"2026-06-19T15:47:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Reformulation invariance on inference problems forces minimization of the Kullback-Leibler divergence, narrowing from f-divergences to alpha-divergences to KL.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19148","ref_index":54,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast Computation of Free-Support Wasserstein Medians","primary_cat":"stat.CO","submitted_at":"2026-06-17T14:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11183","ref_index":63,"ref_count":1,"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":"2606.08721","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Geometric Measure of Linear Separability for Neural Representations","primary_cat":"cs.LG","submitted_at":"2026-06-07T16:31:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.08498","ref_index":137,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Tests for Independence of High-Dimensional Nonstationary Time Series","primary_cat":"math.ST","submitted_at":"2026-06-07T07:54:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06018","ref_index":83,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"On statistical inference for non-linear dynamical systems evolving in their global attractor","primary_cat":"math.ST","submitted_at":"2026-06-04T11:06:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Proves reverse Poincaré inequality on global attractor of 2D reaction-diffusion system to obtain near-parametric statistical recovery of initial conditions from discrete observations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05488","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data","primary_cat":"stat.ML","submitted_at":"2026-06-03T22:26:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02410","ref_index":154,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Optimal sequential two-stage Bayes Factor Design for two-arm clinical Phase II Trials with binary Endpoints","primary_cat":"stat.ME","submitted_at":"2026-06-01T15:53:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Density Evolution: A Multiscale View of Density Estimation","primary_cat":"math.ST","submitted_at":"2026-05-29T18:08:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The cluster-tree literature has studied estimation, pruning, and consistency [14, 15, 28, 69, 70]. Level-set estimation and excess-mass methods provide com- plementary inferential foundations [52, 56, 57, 59, 68, 72]. Density-based algo- rithms such as HDBSCAN, a hierarchical density-based clustering method, can also be interpreted as practical cluster-tree approximations [10]. K. You/Density Evolution19 Fig 4. A two-parameter density-evolution summary. Each point records the number of con- nected components of the superlevel set{x: bfh(x)≥λ}for a smoothing bandwidthhand density levelλ. Classical cluster trees varyλfor one density. Density evolution also varies the smoothing scale. Density evolution adds a second axis."},{"citing_arxiv_id":"2605.30266","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Wasserstein Least Squares: A Canonical Regression Method for Probability Distributions","primary_cat":"math.ST","submitted_at":"2026-05-28T17:28:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Wasserstein least squares extends Euclidean least squares to distribution-valued responses via convex analysis, yielding n^{-1/2} rates under template deformation and faster barycenter rates than prior work.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28952","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Optimal Rates for Differentially Private Hypothesis Testing with E-values","primary_cat":"cs.CR","submitted_at":"2026-05-27T18:00:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Characterizes the optimal e-power for ε-DP e-value hypothesis testing between P^n and Q^n, supplies a matching algorithm, and gives matching bounds on stopping times for private e-processes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25399","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Towards end-to-end LLM-based censoring-aware survival analysis","primary_cat":"cs.AI","submitted_at":"2026-05-25T03:45:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LLMSurvival enables LLM-based survival analysis on tabular data by converting censored time-to-event tasks into pairwise comparisons, yielding small concordance gains over Cox and deep learning baselines on ICU mortality and fracture prediction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08001","ref_index":285,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Scale selection for geometric medians on product manifolds","primary_cat":"math.ST","submitted_at":"2026-05-08T16:57:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07920","ref_index":3,"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":"unclear","context_text":"j=0 (−1)jnjbn−jεj, which is the first expression in Equation (7). For the second expression, the binomial theorem similarly yields εn = 1 n! E[(b−X) n] = 1 n! nX j=0 \u0012n j \u0013 (−1)jbn−jE[X j] = nX j=0 (−1)jbn−j j!(n−j)! E[X j], as desired. Remark.An alternative proof of Corollary 1 sets up the infinite linear system m−b • =A ε where m := (E[X],E [X 2],E [X 3],· · · )⊤, ε := (ε1, ε2, ε3, . . .)⊤, b• := (b, b2, b3, . . .)⊤ andAis the infinite lower triangular matrix A:=   −1 0 0 0· · · −2b2·1 0 0· · · −3b2 3·2b−3·2·1 0· · · −4b3 4·3b 2 −4·3·2b4·3·2·1· · · ... ... ... ... ...   . For each m≥ 1, the m×m upper-left submatrix ofAis lower triangular with diagonal entries −1!, 2!, . . . ,(−1)mm! and hence invertible, and the first m terms of the primitive sequence ε1, ."},{"citing_arxiv_id":"2605.00363","ref_index":241,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space","primary_cat":"math.ST","submitted_at":"2026-05-01T02:54:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21042","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Interpretable Quantile Regression by Optimal Decision Trees","primary_cat":"cs.LG","submitted_at":"2026-04-22T19:40:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A novel algorithm learns sets of optimal quantile regression trees to predict full conditional distributions interpretably and efficiently.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04638","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Joint Estimation in Potts Model","primary_cat":"math.ST","submitted_at":"2026-04-06T12:42:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Sufficient conditions are given for pseudo-likelihood estimation of both parameters in the Potts model at rate sqrt(N) for bounded-degree or irregular graphs, with impossibility shown for certain dense regular graphs, plus a new concentration inequality via nonlinear large deviations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"1 (takingλ= 0) that∥∇ℓN(β,B)∥ 2 =O P( √ N). Part (b) now follows from Proposition E.1 (on takingwN(β,B) =∇ℓ N(β,B),a N = √ Nandh N(X) =N T N(X)) and Lemma F.2. (c) It suffices to check (11), as the other conclusions follow from parts (a) and (b). To this effect, define EN(δ) := \b x∈[q] N :T N(x)< δ . It follows from the tightness ofTN(X) −1, that lim δ→0 sup N⩾1 P(X∈E N(δ)) = 0.(25) Suppose thatx∈E N(δ)c for some fixedδ >0. Fixingx, for notational convenience, we will abbreviateemr,s u (x)byem r,s u . Then, we have: TN(x) = 1 2N 2 X r<s X i,j \u0010 emr,s i −emr,s j \u00112 ⩾δ, Hence, there exist colorsa < b, such that 1 N 2 X i,j \u0010 ema,b i −ema,b j \u00112 ⩾ 4δ q(q−1) .(26) It follows from (8) that for anyr, s∈[q]andi∈[N],em r,s i ∈[−γ, γ], whereγis defined in (8)."},{"citing_arxiv_id":"2504.04267","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient Rejection Sampling in the Entropy-Optimal Range","primary_cat":"cs.DS","submitted_at":"2025-04-05T19:45:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A new sampler for discrete distributions using coin flips that combines linearithmic space, negligible runtime overhead, and entropy-optimal expected flips in [H(P), H(P)+2).","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}