Tests for \(p\)-regression coefficients in linear panel model when \(p\) is divergent
From MaRDI portal
Publication:2023728
DOI10.1007/s10255-020-0947-yzbMath1465.62036OpenAlexW3082268576MaRDI QIDQ2023728
Jing Zhao, Yao-hua Rong, Wei-hu Cheng, Yu Ping Hu, Mi-Xia Wu
Publication date: 3 May 2021
Published in: Acta Mathematicae Applicatae Sinica. English Series (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10255-020-0947-y
Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Causal inference from observational studies (62D20)
Cites Work
- Unnamed Item
- Unnamed Item
- Generalized \(F\) test for high dimensional linear regression coefficients
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
- On exact tests of linear hypothesis in linear models with nested error structure
- Variable selection in nonparametric additive models
- Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates
- Convergence of quadratic forms with nonvanishing diagonal
- Asymptotic properties of bridge estimators in sparse high-dimensional regression models
- Empirical likelihood test for high dimensional linear models
- Marginal asymptotics for the ``large \(p\), small \(n\) paradigm: with applications to microarray data
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Tests for High-Dimensional Regression Coefficients With Factorial Designs
