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.
hub
Zolt´ an Sasv´ ari.Multivariate characteristic and correlation functions , volume 50 of De Gruyter Studies in Mathematics
18 Pith papers cite this work. Polarity classification is still indexing.
18
Pith papers citing it
hub tools
citation-role summary
background 3
citation-polarity summary
roles
background 3polarities
background 3representative citing papers
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.
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.
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.
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.
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.
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.
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.
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.
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.
An iterated I-projection procedure solves the generalized minimum information checkerboard copula problem with convergence guarantees and numerical tests up to dimension four.
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.
SDR augments the Fused Gromov-Wasserstein objective with an explicit dependence term to learn target-aware distributional representations.
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.
Review and simulation comparison of more than 40 threshold selection procedures for univariate extreme value analysis, with application to daily rainfall data.
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.
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.
citing papers explorer
-
An iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem
An iterated I-projection procedure solves the generalized minimum information checkerboard copula problem with convergence guarantees and numerical tests up to dimension four.
-
Proper scoring rules for estimation and forecast evaluation
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.
- How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective