Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 6 - Basic Regression Methods

  • from Part II - Regression Methods for Estimation
  • Darius M. Dziuda, Central Connecticut State University
  • Book:
    Multivariate Biomarker Discovery
    Published online:
    30 May 2024
    Print publication:
    06 June 2024, pp 101-113

  • 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.