{"total":17,"items":[{"citing_arxiv_id":"2606.30864","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Analysis of gradual changes in nonparametric regression based on a new optimization method in the non-unique case","primary_cat":"math.ST","submitted_at":"2026-06-29T19:52:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Develops and compares consistent estimators for gradual change points in nonparametric regression using a new optimization method targeting the largest minimization point of an objective function, with rates, regression estimation, bootstrap, and two-sample extensions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28540","ref_index":21,"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":"2606.20427","ref_index":86,"ref_count":2,"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":"PV X T 2 n + 2(PV X M 2 n + PV X B2 n) , (85) since (x + y)2 \u0014 2(x2 + y2) for all x, y 2 R. For ¯wni in (38), we have by Lemma 3 (iii) that PV Z [ ¯wn,i(v1, x, V, Z, ϑ)] = PV X [wn,i(v1, x, V, X, ϑ)]. Then, in the light of ( 39), the bound (40) follows once we show PV X M 2 n = OPV Z 0 @ 1 n2 X i∈[n] Z V [ ¯wn,i(v1, x, V, Z, ϑ)] dPV1X(v1, x) 1 A . (86) By Markov's inequality , for all K > 0, P PV X M 2 n > K \u0001 \u0014 1 K E \u0014Z Mn(v1, x)2 dPV1X(v1, x) \u0015 = 1 K Z E \u0002 Mn(v1, x)2\u0003 dPV1X(v1, x). For any fixed (v1, x), E [Mn(v1, x)2] = 1 n2 P i∈[n] V [ ¯wn,i(v1, x, V, Z, ϑ)], because the (V ′ i , Zi) are i.i.d.. Hence ( 86) holds. Bound (41). If Q 2 Q δ, then, by Lemma 3, we have for h 2 L2(PV Z ), V \u0002 Q−1 X h(V, Z)"},{"citing_arxiv_id":"2606.19148","ref_index":81,"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":"2606.12185","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pivotal and identification-robust nonparametric inference in linear IV models","primary_cat":"econ.EM","submitted_at":"2026-06-10T15:09:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"New asymptotically pivotal and identification-robust nonparametric tests for parameters in linear IV models handling unknown heteroskedasticity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06233","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Anchor PCA","primary_cat":"stat.ML","submitted_at":"2026-06-04T14:39:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Anchor PCA recovers a maximal invariant subspace for multi-domain data via PCA on a modified target matrix that trades off explained variance with domain agreement.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00293","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo","primary_cat":"cs.LG","submitted_at":"2026-05-29T19:24:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":37,"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":"tistical Association97611-631. [35]Fukunaga, K.andHostetler, L.(1975). The estimation of the gradi- ent of a density function, with applications in pattern recognition.IEEE Transactions on Information Theory2132-40. [36]Genovese, C. R.,Perone-Pacifico, M.,Verdinelli, I.andWasser- man, L.(2014). Nonparametric ridge estimation.The Annals of Statistics 42. [37]Gin 'e, E.andNickl, R.(2010). Confidence bands in density estimation. The Annals of Statistics38. [38]Hall, P.andYork, M.(2001). On the Calibration of Silverman's Test for Multimodality.Statistica Sinica11515-536. [39]Hartigan, J. A.(1975).Clustering algorithms.Wiley series in probability and mathematical statistics. Wiley, New York. [40]Ho, J.,Jain, A."},{"citing_arxiv_id":"2605.31465","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Nonparametric Kiefer-Weiss Problem","primary_cat":"math.ST","submitted_at":"2026-05-29T15:58:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The nonparametric Kiefer-Weiss problem is solved by deriving an optimal stopping policy based on a two-dimensional statistic (likelihood ratio plus expected remaining sample size) whose randomization rule maps the likelihood ratio to an integer sample size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26197","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bimodality in Rotational Modulation of Planet-Hosting 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Transitions in Flat Spectrum Radio Quasars","primary_cat":"astro-ph.HE","submitted_at":"2026-03-15T09:48:46+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Plasmoid reconnection simulations match observed skewness transitions and entropy decreases in FSRQ GeV light curves after flares, with broken power-law PSDs consistent with blazar variability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2012.05220","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Estimating distances from parallaxes. V: Geometric and photogeometric distances to 1.47 billion stars in Gaia Early Data Release 3","primary_cat":"astro-ph.SR","submitted_at":"2020-12-09T18:35:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A public catalogue provides geometric and photogeometric distances plus uncertainties for 1.47 billion Gaia EDR3 stars derived via probabilistic inference with a three-dimensional Galactic prior.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}