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Multivariate Multiple Orthogonal Linear Regression

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Intelligent Decision Technologies 2018 (KES-IDT 2018 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 97))

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Abstract

We propose a multivariate multiple orthogonal linear regression (MMOLR) model. The MMOLR model expresses the relationship between two sets of dependent variables and independent variables. It is possible to use the MMOLR as a step to be followed by multivariate linear regression to compare and investigate the relationships between dependent variables, which are still limited in the multivariate linear regression model. The MMOLR takes into account the advantages of the errors-in-variables model, that is, total least squares, and thereby, examines the errors of all independent and dependent variables. Consequently, the assumptions of the model are easy to satisfy in practice. Moreover, in the context of total least squares, we reveal the relationship between the MMOLR and the canonical regression model.

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Author information

Authors and Affiliations

  1. Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan

    Thi Binh An Duong & Jun Tsuchida

  2. Department of Culture and Information Science, Doshisha University, Kyoto, Japan

    Hiroshi Yadohisa

Authors
  1. Thi Binh An Duong
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  2. Jun Tsuchida
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  3. Hiroshi Yadohisa
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Corresponding author

Correspondence to Thi Binh An Duong .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University, Poole, United Kingdom

    Robert J. Howlett

  3. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia

    Lakhmi C. Jain

  4. Griffith Sciences - Centres and Institutes, Griffith University, South Brisbane, Queensland, Australia

    Ljubo Vlacic

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An Duong, T.B., Tsuchida, J., Yadohisa, H. (2019). Multivariate Multiple Orthogonal Linear Regression. In: Czarnowski, I., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Decision Technologies 2018. KES-IDT 2018 2018. Smart Innovation, Systems and Technologies, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-92028-3_5

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