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Detecting Multiple Influential Observations in High Dimensional Linear Regression

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Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

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Abstract

In this paper, we consider the detection of multiple influential observations in high dimensional regression, where the p number of covariates is much larger than sample size n. Detection of influential observations in high dimensional regression is challenging. In the case of single influential observation, Zhao et al. (2013) developed a method called High dimensional Influence Measure (HIM). However, the result of HIM is not applicable to the case of multiple influential observations, where the detection of influential observations is much more complicated than the case of single influential observation. We propose in this paper a new method based on the multiple deletion to detect the multiple influential.

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Acknowledgements

The research of Zhao was supposed by National Science Foundation of China (No. 11471030, 11101022) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. LMIB of the Ministry of Education, School of Systems and Mathematics, Beihang University, Beijing, China

    Junlong Zhao, Ying Zhang & Lu Niu

Authors
  1. Junlong Zhao
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  2. Ying Zhang
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  3. Lu Niu
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Corresponding author

Correspondence to Junlong Zhao .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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Zhao, J., Zhang, Y., Niu, L. (2015). Detecting Multiple Influential Observations in High Dimensional Linear Regression. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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