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Linear Regression and Its Cousins

In: Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

    (Pfizer Global Research and Development, Division of Nonclinical Statistics)

  • Kjell Johnson

    (Arbor Analytics)

Abstract

In this chapter we discuss several models, all of which are akin to linear regression in that each can directly or indirectly be written in the widely know multiple linear regression form. We begin this chapter by describing a chemistry case study data set (Section 6.1) which will be used to illustrate models throughout this chapter as well as for Chapters 7-9. As a foundational model, we discuss ordinary linear regression (Section 6.2). Section 6.3 defines and illustrates partial least squares and its algorithmic and computational variations. Penalized models such as ridge regression, the lasso, and the elastic net are presented in Section 6.4. In the Computing Section (6.5) we demonstrate how to train each of these models in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "Linear Regression and Its Cousins," Springer Books, in: Applied Predictive Modeling, chapter 0, pages 101-139, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6849-3_6
    DOI: 10.1007/978-1-4614-6849-3_6
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