A study of sensitivity analysis on the method of principal Hessian directions

DOI10.1007/S001800100054zbMath1007.62055OpenAlexW3939841MaRDI QIDQ1861593

Lue Heng-Hui

Publication date: 9 March 2003

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s001800100054

zbMATH Keywords

perturbationdimension reductionnonlinear principal components

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Point estimation (62F10)

Related Items (4)

Improving estimated sufficient summary plots in dimension reduction using minimization criteria based on initial estimates ⋮ A METHOD OF LOCAL INFLUENCE ANALYSIS IN SUFFICIENT DIMENSION REDUCTION ⋮ Trimming influential observations for improved single-index model estimated sufficient summary plots ⋮ Influence Functions for Dimension Reduction Methods: An Example Influence Study of Principal Hessian Direction Analysis

Cites Work

• On almost linearity of low dimensional projections from high dimensional data
• Slicing regression: A link-free regression method
• An asymptotic theory for sliced inverse regression
• Principal Hessian Directions Revisited
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• A Systematic Approach to the Analysis of Complex Interaction Patterns in Two-Level Factorial Designs
• Interactive Tree-Structured Regression via Principal Hessian Directions
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