Cover Image

With random regressors, least squares inference is robust to correlated errors with unknown correlation structure

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Zifeng, Ding, Peng, Zhou, Wen, Wang, Haonan
Format:	Preprint
Published:	2024
Subjects:	Statistics Theory Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Subscedastic weighted least squares estimates
by: Bryan, Jordan, et al.
Published: (2024)

A generalized Bayesian approach for high-dimensional robust regression with serially correlated errors and predictors
by: Chakraborty, Saptarshi, et al.
Published: (2024)

Using prior information to boost power in correlation structure support recovery
by: Ding, Ziyang, et al.
Published: (2021)

The Generalized Elastic Net for least squares regression with network-aligned signal and correlated design
by: Tran, Huy, et al.
Published: (2022)

Sufficient dimension reduction for regression with spatially correlated errors: application to prediction
by: Forzani, Liliana, et al.
Published: (2025)

Two-stage weighted least squares estimator of multivariate non-negative observation-driven models
by: Armillotta, Mirko
Published: (2023)

Doubly robust inference with censoring unbiased transformations
by: Sandqvist, Oliver Lunding
Published: (2024)

Neymanian inference in randomized experiments
by: Chattopadhyay, Ambarish, et al.
Published: (2024)

On the partial autocorrelation function for locally stationary time series: characterization, estimation and inference
by: Ding, Xiucai, et al.
Published: (2024)

Optimal inference for the mean of random functions
by: Kassi, Omar, et al.
Published: (2025)

Asymptotics of cut distributions and robust modular inference using Posterior Bootstrap
by: Pompe, Emilia, et al.
Published: (2021)

Deep neural expected shortfall regression with tail-robustness
by: Yu, Myeonghun, et al.
Published: (2025)

Properly constrained reference priors decay rates for efficient and robust posterior inference
by: Van Biesbroeck, Antoine
Published: (2024)

Doubly robust inference via calibration
by: van der Laan, Lars, et al.
Published: (2024)

Doubly robust estimation with functional outcomes missing at random
by: Liu, Xijia, et al.
Published: (2024)

Partial correlation networks of Gaussian processes
by: Peruzzi, Michele
Published: (2026)

Unifying regression-based and design-based causal inference in time-series experiments
by: Lin, Zhexiao, et al.
Published: (2025)

On inference for modularity statistics in structured networks
by: Mitra, Anirban, et al.
Published: (2024)

Copula-based models for correlated circular data
by: Lagona, Francesco, et al.
Published: (2024)

Modelling pairs of Poissons and binomials with negative correlation
by: Hjort, Nils Lid
Published: (2026)

Completing and studentising Spearman's correlation in the presence of ties
by: Hurley, Landon
Published: (2025)

Sparsity meets correlation in Gaussian sequence model
by: Kotekal, Subhodh, et al.
Published: (2023)

Statistical inference of random graphs with a surrogate likelihood function
by: Wu, Dingbo, et al.
Published: (2022)

Design-based inference for generalized causal effects in randomized experiments
by: Chen, Xinyuan, et al.
Published: (2026)

A spectral inference method for determining the number of communities in networks
by: Wu, Yujia, et al.
Published: (2026)

Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)

Generalizing and transporting causal inferences from randomized trials in the presence of trial engagement effects
by: Ung, Lawson, et al.
Published: (2024)

Expansion of net correlations in terms of partial correlations
by: Arenaza, Bautista, et al.
Published: (2024)

A direct extension of Azadkia & Chatterjee's rank correlation to multi-response vectors
by: Ansari, Jonathan, et al.
Published: (2022)

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

Efficient algorithms for the sensitivities of the Pearson correlation coefficient and its statistical significance to online data
by: Harary, Marc
Published: (2024)

Bias correction for Chatterjee's graph-based correlation coefficient
by: Azadkia, Mona, et al.
Published: (2025)

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

Two-sample inference for sparse functional data
by: Zhang, Chi, et al.
Published: (2023)

Coverage correlation: detecting singular dependencies between random variables
by: Yang, Xuzhi, et al.
Published: (2025)

Asymptotically-exact selective inference for quantile regression
by: Wang, Yumeng, et al.
Published: (2024)

Ledoit-Wolf linear shrinkage with unknown mean
by: Oriol, Benoit, et al.
Published: (2023)

Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs
by: Idrissi, Marouane Il, et al.
Published: (2021)

Robust estimation of polyserial correlation coefficients: A density power divergence approach
by: Welz, Max
Published: (2025)

Pearson Chi-squared Conditional Randomization Test
by: Javanmard, Adel, et al.
Published: (2021)