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Prediction Intervals of Principal Component Regression with Applications to Molecular Descriptors Datasets

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2015))

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Abstract

The prediction interval of the Principal Component Regression (PCR) model is usually based on some strong distributional assumptions. To overcome this drawback, this study extends a prediction interval estimation method by considering a condition of distribution-free. Six different prediction interval estimation methods are developed for constructing prediction intervals for PCR models. The simulated and real data experiment results show that the developed methods perform better than some state-of-the-art methods. This study can enrich the tools of PCR model prediction and statistical inference and is significant for data analysis.

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Acknowledgment

We are grateful to the anonymous reviewers and editors for their constructive comments that help us improve quality of this manuscript. This work is financially supported by the National Innovation Project of University Students (202110595046).

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Authors and Affiliations

  1. School of Mathematics and Computational Sciences, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, 541002, Guangxi, China

    Yuling Fu, Zixin Bin & Youwu Lin

  2. Center for Applied Mathematics of Guangxi (GUET), Guilin, 541002, People’s Republic of China

    Yuling Fu & Youwu Lin

  3. Guangxi Academy of Sciences High-Tech Group Co., LTD., Nanning, 530007, Guangxi, China

    Ligong Wei

Authors
  1. Yuling Fu
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  2. Zixin Bin
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  3. Ligong Wei
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  4. Youwu Lin
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Corresponding authors

Correspondence to Ligong Wei or Youwu Lin .

Editor information

Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Guangxi Academy of Sciences, Guangxi, China

    Changan Yuan

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Fu, Y., Bin, Z., Wei, L., Lin, Y. (2024). Prediction Intervals of Principal Component Regression with Applications to Molecular Descriptors Datasets. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_19

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  • DOI: https://doi.org/10.1007/978-981-97-0827-7_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0826-0

  • Online ISBN: 978-981-97-0827-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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