{"total":4,"items":[{"citing_arxiv_id":"2607.06089","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Closed-form fractional radial links for elliptical Mahalanobis discriminant analysis","primary_cat":"math.ST","submitted_at":"2026-07-07T10:02:33+00:00","verdict":"CONDITIONAL","verdict_confidence":"UNKNOWN","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The Bayes-optimal classifier for elliptical distributions is derived in closed form from the radial generator, yielding a tuning-free alternative to spline GAMs with proven consistency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20427","ref_index":172,"ref_count":1,"confidence":0.88,"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":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09391","ref_index":33,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Kling-Gupta linear regression","primary_cat":"math.ST","submitted_at":"2026-06-08T12:06:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Kling-Gupta linear regression scales the OLS coefficient vector by a variance-inflation factor based on sample moments, preserves response variance on the training set, and converges almost surely to explicit population limits while maximizing KGE but not NSE.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22512","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications","primary_cat":"math.NA","submitted_at":"2026-04-24T12:52:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"+ ϵ1/2|Θ×[0, T]| 1/2 m is a more direct bound, also satisfying the upcoming convergence. However, we state this in regard to the former definition to stay consistent with the later discussion surrounding Assumption 1, that is formulated inL ∞-norm. From here, we assert thatE S →0 almost surely forN s2 → ∞by using the uniform strong law of large numbers [35]. In fact, we calculate ˆf(ν;µ, t) :=∥u µ,ν,t h −V µ,tVT µ,tGuµ,ν,t h ∥2 ≤M 2∥INh −V µ,tVT µ,tG∥2 op ≤M 2 <∞, uniformly bounded and continuous in (µ, t)∈Θ×[0, T] by Lemma 1. Assumption 2 gives compactness of Θ×[0, T]. Hence, we use the mentioned uniform law, to get a bound on the supremum, i.e. sup (µ,t)∈Θ×[0,T] Z Θ′ ˆf(ν;µ, t)d(ν)− |Θ′| Ns2"}],"limit":50,"offset":0}