On Sliced Inverse Regression With High-Dimensional Covariates

By Lixing Zhu, Bai-qi Miao, Heng Peng. 2006

289 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liping Zhu
2. Lingzhou Xue
3. Yanyuan Ma
4. Lixing Zhu
5. Jiawei Yao
6. Bing Li
7. Song-qiao Wen
8. Lixing Zhu

In Statistics and Probability

1. Lixing Zhu
2. Liping Zhu
3. Lingzhou Xue
4. Jiawei Yao
5. Yanyuan Ma
6. Jianqing Fan
7. Yingxing Li
8. Li-Xing Zhu

Citations in the corpus

• Sufficient Forecasting Using Factor Models (2015, background): “It is well-known that the traditional sliced inverse regression and many other dimension reduction methods are limited to either a fixed dimension or a diverging dimension that is smaller than the sample size (Zhu et al., 2006).”
• Asymptotics for sliced average variance estimation (2007, background): “A relevant reference is Zhu, Miao and Peng [26].”
• Consistently determining the number of factors in multivariate volatility modelling (2015, methodology): “…reduction, the corresponding eigenvectors that are associated with the nonzero eigenvalues of a target matrix are used as the base vectors of the space we want to determine, see Cook (1998), and relevant references are in Zhu, Miao, and Peng (2006), Zhu and Zhu (2009), and Feng et al. (2013).”
• On a Projective Resampling Method for Dimension Reduction With Multivariate Responses (2008, methodology): “The consistency of this selection criterion is demonstrated in Zhu et al. (2006).”
• Inverse Moment Methods for Sufficient Forecasting Using High-Dimensional Predictors (2016, unknown): “A commonly recognized limit of sufficient dimension reduction methods, including sliced inverse regression, is that they can only handle predictors with either a finite dimension or a diverging dimension that is dramatically smaller than the sample size (Zhu et al., 2006).”
• On estimation efficiency of the central mean subspace (2014, methodology): “…test procedure in Cook and Li (2004), the crossvalidation procedure in Xia et al. (2002) and Xia (2007), the Bayesian information criterion BIC type of criterion in Zhu et al. (2006) and the bootstrap procedure in Ye and Weiss (2003), Li and Dong (2009), Dong and Li (2010), Ma and Zhu (2012), etc.”