Cover Image

Latent confounding in high-dimensional nonlinear models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Yuhao, Shah, Rajen
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders
by: Wang, Yuhao, et al.
Published: (2020)

Average partial effect estimation using double machine learning
by: Klyne, Harvey, et al.
Published: (2023)

Sandwich Boosting for Accurate Estimation in Partially Linear Models for Grouped Data
by: Young, Elliot H., et al.
Published: (2023)

Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values
by: Guo, F. Richard, et al.
Published: (2023)

Cross-validation for change-point regression: pitfalls and solutions
by: Pein, Florian, et al.
Published: (2021)

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

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

Outrigger local polynomial regression
by: Young, Elliot H., et al.
Published: (2026)

An average-case sensitivity analysis for unmeasured confounding
by: Zhang, Yao, et al.
Published: (2022)

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

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

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

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

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

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

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

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

Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data
by: Lyu, Zhongyuan, et al.
Published: (2024)

Testing common structure in high-dimensional factor models: change-point and two-sample procedures
by: Düker, Marie-Christine, et al.
Published: (2024)

Asymptotic Theory of the Best-Choice Rerandomization using the Mahalanobis Distance
by: Wang, Yuhao, et al.
Published: (2023)

Rerandomization for quantile treatment effects
by: Han, Tingxuan, et al.
Published: (2026)

Optimal covariance matrix estimation for high-dimensional noise in high-frequency data
by: Chang, Jinyuan, et al.
Published: (2018)

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

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

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

Residual permutation test for regression coefficient testing
by: Wen, Kaiyue, et al.
Published: (2022)

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

The Projected Covariance Measure for assumption-lean variable significance testing
by: Lundborg, Anton Rask, et al.
Published: (2022)

A multivariate extension of Azadkia-Chatterjee's rank coefficient
by: Huang, Wenjie, et al.
Published: (2025)

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

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

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

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

Trade-off between predictive performance and FDR control for high-dimensional Gaussian model selection
by: Lacroix, Perrine, et al.
Published: (2023)

Improved confidence intervals for nonlinear mixed-effects and nonparametric regression models
by: Zheng, Nan, et al.
Published: (2024)

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

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

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

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

A note on numerical evaluation of conditional Akaike information for nonlinear mixed-effects models
by: Zheng, Nan, et al.
Published: (2024)