Generalized principal Hessian directions for mixture multivariate skew elliptical distributions
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Publication:1795575
DOI10.1016/j.jmva.2018.07.006zbMath1408.62112OpenAlexW2887628635WikidataQ129448159 ScholiaQ129448159MaRDI QIDQ1795575
Fei Chen, Lei Shi, Xuehu Zhu, Li Xing Zhu
Publication date: 16 October 2018
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2018.07.006
sufficient dimension reductionprincipal Hessian directionsStein's lemmaskew elliptical distributionsgeneralized principal Hessian directions
Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
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Cites Work
- Unnamed Item
- Multivariate mixture modeling using skew-normal independent distributions
- Dimension reduction for nonelliptically distributed predictors
- Analysis of multivariate skew normal models with incomplete data
- Slicing regression: A link-free regression method
- Transformation-based estimation
- Maximum likelihood estimation for multivariate skew normal mixture models
- Consistently determining the number of factors in multivariate volatility modelling
- Sufficient dimension reduction with mixture multivariate skew elliptical distributions
- Dimension reduction for non-elliptically distributed predictors: second-order methods
- On Directional Regression for Dimension Reduction
- Sliced Inverse Regression for Dimension Reduction
- On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
- Reweighting to Achieve Elliptically Contoured Covariates in Regression
- The multivariate skew-normal distribution
- On the second-order inverse regression methods for a general type of elliptical predictors
- Dimension Reduction in Regressions Through Cumulative Slicing Estimation
- Combining eigenvalues and variation of eigenvectors for order determination
- On Estimation Efficiency of the Central Mean Subspace
- On Sliced Inverse Regression With High-Dimensional Covariates
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