The relative effects of dimensionality and multiplicity of hypotheses on the \(F\)-test in linear regression

DOI10.1214/16-EJS1186zbMath1345.62040arXiv1511.06268OpenAlexW3098592783MaRDI QIDQ315402

Lukas Steinberger

Publication date: 21 September 2016

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.06268

zbMATH Keywords

multiple hypothesis testing\(F\)-testhigh-dimensional linear regressionlarge-\(p\) asymptotics

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03) Central limit and other weak theorems (60F05) Random matrices (probabilistic aspects) (60B20) Random matrices (algebraic aspects) (15B52) Asymptotic properties of parametric tests (62F05)

Related Items

STATISTICAL INFERENCE WITH F-STATISTICS WHEN FITTING SIMPLE MODELS TO HIGH-DIMENSIONAL DATA

Uses Software

• R

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Covariance estimation for distributions with \({2+\varepsilon}\) moments
• Generalized \(F\) test for high dimensional linear regression coefficients
• Limit of the smallest eigenvalue of a large dimensional sample covariance matrix
• Conditional predictive inference post model selection
• On the central limit theorem in \(R^ p\) when p\(\rightarrow \infty\)
• Asymptotic behavior of likelihood methods for exponential families when the number of parameters tends to infinity
• On the theory of elliptically contoured distributions
• The distribution and moments of the smallest eigenvalue of a random matrix of Wishart type
• Analytic properties of noncentral distributions
• Properties of Schur complements in partitioned idempotent matrices.
• The normal distribution. Characterizations with applications
• Linear models. Least squares and alternatives
• Convergence of quadratic forms with nonvanishing diagonal
• On conditional moments of high-dimensional random vectors given lower-dimensional projections
• Shrinkage Estimators for Prediction Out-of-Sample: Conditional Performance
• Tests for High-Dimensional Regression Coefficients With Factorial Designs
• On low-dimensional projections of high-dimensional distributions

This page was built for publication: The relative effects of dimensionality and multiplicity of hypotheses on the \(F\)-test in linear regression