Most powerful test against a sequence of high dimensional local alternatives

DOI10.1016/j.jeconom.2021.10.015OpenAlexW4206805365MaRDI QIDQ2697980

Sombut Jaidee, Yi He, J. T. Gao

Publication date: 14 April 2023

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2021.10.015

zbMATH Keywords

hypothesis testingnuisance parameterrandom matrix theoryhigh-dimensional linear modeluniformly powerful test

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Uses Software

• hdi

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Innovated higher criticism for detecting sparse signals in correlated noise
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Nonlinear shrinkage estimation of large-dimensional covariance matrices
• Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control
• Nonparametric eigenvalue-regularized precision or covariance matrix estimator
• Statistical significance in high-dimensional linear models
• Generalized \(F\) test for high dimensional linear regression coefficients
• On the estimation of integrated covariance matrices of high dimensional diffusion processes
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Asymptotics of the principal components estimator of large factor models with weakly influential factors
• Eigenvectors of some large sample covariance matrix ensembles
• Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
• Limiting spectral distribution of large-dimensional sample covariance matrices generated by VARMA
• Spectral analysis of large dimensional random matrices
• Concentration of measure and spectra of random matrices: applications to correlation matrices, elliptical distributions and beyond
• A central limit theorem for generalized quadratic forms
• No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices
• Test for high-dimensional regression coefficients using refitted cross-validation variance estimation
• High-dimensional simultaneous inference with the bootstrap
• High-dimensional asymptotics of prediction: ridge regression and classification
• On the inference about the spectral distribution of high-dimensional covariance matrix based on high-frequency noisy observations
• Limiting spectral distribution for a class of random matrices
• Higher criticism for detecting sparse heterogeneous mixtures.
• On the empirical distribution of eigenvalues of a class of large dimensional random matrices
• Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices
• Convergence of the largest eigenvalue of normalized sample covariance matrices when \(p\) and \(n\) both tend to infinity with their ratio converging to zero
• Detection boundary in sparse regression
• Convergence of eigenvector empirical spectral distribution of sample covariance matrices
• On the Marčenko-Pastur law for linear time series
• On asymptotics of eigenvectors of large sample covariance matrix
• Convergence rates of eigenvector empirical spectral distribution of large dimensional sample covariance matrix
• Tests alternative to higher criticism for high-dimensional means under sparsity and column-wise dependence
• Comparison between two types of large sample covariance matrices
• Variance estimation in high-dimensional linear models
• A LIMIT THEOREM FOR QUADRATIC FORMS AND ITS APPLICATIONS
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Weak and strong cross‐section dependence and estimation of large panels
• Linear Hypothesis Testing in Dense High-Dimensional Linear Models
• Testing Against a High Dimensional Alternative
• Forecasting Using Principal Components From a Large Number of Predictors
• Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• Power Enhancement in High-Dimensional Cross-Sectional Tests
• Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
• High-Dimensional Probability
• Testing Independence Among a Large Number of High-Dimensional Random Vectors
• Inference on Causal and Structural Parameters using Many Moment Inequalities
• Power in High‐Dimensional Testing Problems
• Tests for High-Dimensional Regression Coefficients With Factorial Designs
• Tests for High Dimensional Generalized Linear Models
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
• High Dimensional Correlation Matrices: The Central Limit Theorem and Its Applications
• Economic Predictions With Big Data: The Illusion of Sparsity