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6 - Basic Regression Methods

from Part II - Regression Methods for Estimation

Published online by Cambridge University Press:  30 May 2024

Darius M. Dziuda
Affiliation:
Central Connecticut State University
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Summary

Chapter 6 starts with the description of multiple regression. Even if it is unlikely for multiple regression to be used as the primary method for multivariate biomarker discovery based on high-dimensional data, presenting this classical method provides the necessary background for regression analysis and highlights the weaknesses of multiple regression, which will be addressed by the subsequently presented methods. This chapter also presents partial least squares regression (PLSR), which by performing supervised dimensionality reduction addresses some weaknesses of multiple regression; however, by not performing any feature selection, PLSR does not reduce noise that is typically abundant in high-dimensional data.

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Chapter
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Multivariate Biomarker Discovery
Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
, pp. 101 - 113
Publisher: Cambridge University Press
Print publication year: 2024

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  • Basic Regression Methods
  • Darius M. Dziuda, Central Connecticut State University
  • Book: Multivariate Biomarker Discovery
  • Online publication: 30 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009006767.009
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  • Basic Regression Methods
  • Darius M. Dziuda, Central Connecticut State University
  • Book: Multivariate Biomarker Discovery
  • Online publication: 30 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009006767.009
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Basic Regression Methods
  • Darius M. Dziuda, Central Connecticut State University
  • Book: Multivariate Biomarker Discovery
  • Online publication: 30 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009006767.009
Available formats
×