Comparison of Partial Least Squares Regression and Principal Component Regression for Overcoming Multicollinearity in Human Development Index Model

  • Samosir R
  • Salaki D
  • Langi Y
N/ACitations
Citations of this article
11Readers
Mendeley users who have this article in their library.

Abstract

One of the assumptions in ordinary least squares (OLS) in estimating regression parameter is lack of multicollinearity. If the multicollinearity exists, Partial Least Square (PLS) and Principal Component Regression (PCR) can be used as alternative approaches to solve the problem. This research intends to compare those methods in modeling factors that influence the Human Development Index (HDI) of North Sumatra Province in 2019 obtained from the Central Bureau of Statistics. The result indicates that the PLS outperforms the PCR in term of  the coefficient of determination and squared error

Cite

CITATION STYLE

APA

Samosir, R. D., Salaki, D. T., & Langi, Y. (2022). Comparison of Partial Least Squares Regression and Principal Component Regression for Overcoming Multicollinearity in Human Development Index Model. Operations Research: International Conference Series, 3(1), 1–7. https://doi.org/10.47194/orics.v3i1.126

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free