Imagen de Portada

Optimal covariance matrix estimation for high-dimensional noise in high-frequency data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Chang, Jinyuan, Hu, Qiao, Liu, Cheng, Tang, Cheng Yong
Formato:	Preprint
Publicado:	2018
Materias:	Statistics Theory Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

An autocovariance-based learning framework for high-dimensional functional time series
por: Chang, Jinyuan, et al.
Publicado: (2020)

Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and $p$-values
por: Chang, Jinyuan, et al.
Publicado: (2025)

Statistical inference for high-dimensional spectral density matrix
por: Chang, Jinyuan, et al.
Publicado: (2022)

On the modelling and prediction of high-dimensional functional time series
por: Chang, Jinyuan, et al.
Publicado: (2024)

Weight-calibrated estimation for factor models of high-dimensional time series
por: Qiao, Xinghao, et al.
Publicado: (2025)

Edge differentially private estimation in the $β$-model via jittering and method of moments
por: Chang, Jinyuan, et al.
Publicado: (2021)

Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization
por: Chang, Jinyuan, et al.
Publicado: (2025)

Spectral analysis of high-dimensional spot volatility matrix with applications
por: Liu, Qiang, et al.
Publicado: (2025)

Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
por: Lu, Xin, et al.
Publicado: (2023)

Dimension estimation in PCA model using high-dimensional data augmentation
por: Radojicic, Una, et al.
Publicado: (2025)

Optimal rates for estimating the covariance kernel from synchronously sampled functional data
por: Berger, Max, et al.
Publicado: (2024)

Asymptotic testing of covariance separability for matrix elliptical data
por: Virta, Joni, et al.
Publicado: (2026)

Difference-based covariance matrix estimate in time series nonparametric regression with applications to specification tests
por: Bai, Lujia, et al.
Publicado: (2023)

Empirical Bayes inference in sparse high-dimensional generalized linear models
por: Tang, Yiqi, et al.
Publicado: (2023)

Functional knockoffs selection with applications to functional data analysis in high dimensions
por: Qiao, Xinghao, et al.
Publicado: (2024)

Testing independence in the presence of missing data: high-dimensional case
por: Cuparić, Marija, et al.
Publicado: (2026)

A nonparametric test of spherical symmetry applicable to high dimensional data
por: Banerjee, Bilol, et al.
Publicado: (2024)

Uncertainty of high-dimensional genetic data prediction with polygenic risk scores
por: Fu, Haoxuan, et al.
Publicado: (2024)

Robust mean change point testing in high-dimensional data with heavy tails
por: Li, Mengchu, et al.
Publicado: (2023)

Exact distribution-free tests of spherical symmetry applicable to high dimensional data
por: Banerjee, Bilol, et al.
Publicado: (2024)

Efficient nonparametric estimation of Toeplitz covariance matrices
por: Klockmann, Karolina, et al.
Publicado: (2023)

Latent confounding in high-dimensional nonlinear models
por: Wang, Yuhao, et al.
Publicado: (2025)

Tests for white noise via asymptotically independent U-statistics in high-dimensions
por: Xu, Yuanya
Publicado: (2026)

Asymmetric canonical correlation analysis of Riemannian and high-dimensional data
por: Buenfil, James, et al.
Publicado: (2024)

Two-Stage Testing in a high dimensional setting
por: Jonker, Marianne A, et al.
Publicado: (2024)

Adaptive L-statistics for high dimensional test problem
por: Ma, Huifang, et al.
Publicado: (2024)

HEDE: Heritability estimation in high dimensions by Ensembling Debiased Estimators
por: Song, Yanke, et al.
Publicado: (2024)

Regression adjustment in covariate-adaptive randomized experiments with missing covariates
por: Fu, Wanjia, et al.
Publicado: (2025)

Large-scale Multiple Testing of Cross-covariance Functions with Applications to Functional Network Models
por: Fang, Qin, et al.
Publicado: (2024)

Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity
por: Liu, Lin, et al.
Publicado: (2023)

Optimal estimation in private distributed functional data analysis
por: Xue, Gengyu, et al.
Publicado: (2024)

Prediction-based inference for integrated diffusions with high-frequency data
por: Jørgensen, Emil S., et al.
Publicado: (2026)

Detection and inference of changes in high-dimensional linear regression with non-sparse structures
por: Cho, Haeran, et al.
Publicado: (2024)

Test for high-dimensional mean vectors via the weighted $L_2$-norm
por: Li, Jianghao, et al.
Publicado: (2024)

The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
por: Moosavi, Niloofar, et al.
Publicado: (2021)

Low-rank matrix estimation via nonconvex spectral regularized methods in errors-in-variables matrix regression
por: Li, Xin, et al.
Publicado: (2024)

Covariance scanning for adaptively optimal change point detection in high-dimensional linear models
por: Cho, Haeran, et al.
Publicado: (2025)

Fitting sparse high-dimensional varying-coefficient models with Bayesian regression tree ensembles
por: Ghosh, Soham, et al.
Publicado: (2025)

Strong Gaussian approximation for U-statistics in high dimensions and beyond
por: Li, Weijia, et al.
Publicado: (2026)

Splitting schemes and estimators for stochastic differential equations with Hölder multiplicative noise
por: Fang, Bowen, et al.
Publicado: (2026)