High-Dimensional Gaussian Copula Regression: Adaptive Estimation and Statistical Inference

DOI10.5705/ss.202016.0041zbMath1390.62099arXiv1512.02487OpenAlexW2963721244MaRDI QIDQ4639587

Linjun Zhang, T. Tony Cai

Publication date: 9 May 2018

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1512.02487

zbMATH Keywords

hypothesis testingconfidence intervalKendall's tauadaptive estimationlinear regressionoptimal rate of convergenceGaussian copula regressionde-biased estimator

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Parametric tolerance and confidence regions (62F25) Hypothesis testing in multivariate analysis (62H15) Applications of statistics to social sciences (62P25) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (10)

A convex optimization approach to high-dimensional sparse quadratic discriminant analysis ⋮ Variable selection for high dimensional Gaussian copula regression model: an adaptive hypothesis testing procedure ⋮ Gaussian copula function-on-scalar regression in reproducing kernel Hilbert space ⋮ Coordinatewise Gaussianization: Theories and Applications ⋮ Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks ⋮ Detection of block-exchangeable structure in large-scale correlation matrices ⋮ Robust feature screening for elliptical copula regression model ⋮ ROS regression: integrating regularization with optimal scaling regression ⋮ Robust feature screening for multi-response trans-elliptical regression model with ultrahigh-dimensional covariates ⋮ On the uniform-in-bandwidth consistency of the general conditionalU-statistics based on the copula representation

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