Corrections to test statistics in principal Hessian directions

DOI10.1016/S0167-7152(99)00182-0zbMath1157.62412MaRDI QIDQ1977637

Jun Xie, Peter M. Bentler

Publication date: 17 May 2000

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

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Factor analysis and principal components; correspondence analysis (62H25)

Related Items (15)

A comparison of efficient approximations for a weighted sum of chi-squared random variables ⋮ A link-free approach for testing common indices for three or more multi-index models ⋮ Assessing Corrections to the Weighted Chi-Squared Test for Dimension ⋮ Fourier transform approach for inverse dimension reduction method ⋮ Dimension estimation in sufficient dimension reduction: a unifying approach ⋮ Using sliced inverse mean difference for sufficient dimension reduction ⋮ On model-free conditional coordinate tests for regressions ⋮ Iterative application of dimension reduction methods ⋮ Dimension reduction for the conditional mean in regressions with categorical predictors ⋮ Using sliced mean variance-covariance inverse regression for classification and dimension reduction ⋮ On testing common indices for two multi-index models: a link-free approach ⋮ Sliced inverse moment regression using weighted chi-squared tests for dimension reduction ⋮ Bootstrap Testing of the Rank of a Matrix via Least-Squared Constrained Estimation ⋮ An Empirical Process View of Inverse Regression ⋮ Asymptotic distributions for testing dimensionality in \(q\)-based pHd.

Cites Work

• Unnamed Item
• On chi-squared tests for multiway contingency tables with cell proportions estimated from survey data
• Synthesis of variance
• An F Approximation to the Distribution of a Linear Combination of Chi-squared Variables.
• Principal Hessian Directions Revisited
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification

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