Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.
month = mar, year =
7 Pith papers cite this work, alongside 36 external citations. Polarity classification is still indexing.
7
Pith papers citing it
36
external citations · OpenAlex
verdicts
UNVERDICTED 7representative citing papers
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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.
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.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
citing papers explorer
-
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.
-
Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
-
Differentially private hypothesis testing in survival analysis
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
-
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
-
Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
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.
-
Scale selection for geometric medians on product manifolds
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.
-
Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.