A bootstrap method for assessing the dimension of a general regression problem

DOI10.1016/J.SPL.2006.07.020zbMath1126.62026OpenAlexW2052095699MaRDI QIDQ871006

M. Pilar Barrios, Santiago Velilla

Publication date: 15 March 2007

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2006.07.020

zbMATH Keywords

eigenvaluesresamplingsliced inverse regression (SIR)dimension reduction subspacesnormal probability plots

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Hypothesis testing in multivariate analysis (62H15) Linear inference, regression (62J99) Nonparametric statistical resampling methods (62G09)

Related Items (10)

A Sliced Inverse Regression Approach for a Stratified Population ⋮ A new sliced inverse regression method for multivariate response ⋮ Student sliced inverse regression ⋮ Variable importance assessment in sliced inverse regression for variable selection ⋮ A sliced inverse regression approach for data stream ⋮ Bagging Versions of Sliced Inverse Regression ⋮ Cluster-based sliced inverse regression ⋮ Bootstrap Testing of the Rank of a Matrix via Least-Squared Constrained Estimation ⋮ An Empirical Process View of Inverse Regression ⋮ Overlapping sliced inverse regression for dimension reduction

Uses Software

• ARC

Cites Work

• Correction to: Bootstrap tests and confidence regions for functions of a covariance matrix
• Some asymptotic theory for the bootstrap
• On Wielandt's inequality and its application to the asymptotic distribution of the eigenvalues of a random symmetric matrix
• Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests. (With comments)
• The jackknife and bootstrap
• Sliced Inverse Regression for Dimension Reduction
• Extending Sliced Inverse Regression
• A Model-Free Test for Reduced Rank in Multivariate Regression
• Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
• Theory & Methods: Special Invited Paper: Dimension Reduction and Visualization in Discriminant Analysis (with discussion)
• Assessing the Number of Linear Components in a General Regression Problem
• Dimension Reduction in Binary Response Regression
• Identifying Regression Outliers and Mixtures Graphically
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