PERBANDINGAN ANALISIS LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR DAN PARTIAL LEAST SQUARES (Studi Kasus: Data Microarray)

  • KADEK DWI FARMANI Universitas Udayana
  • I PUTU EKA NILA KENCANA Universitas Udayana
  • KOMANG GDE SUKARSA Universitas Udayana

Abstract

Linear regression analysis is one of the parametric statistical methods which utilize the relationship between two or more quantitative variables. In linear regression analysis, there are several assumptions that must be met that is normal distribution of errors, there is no correlation between the error and error variance is constant and homogent. There are some constraints that caused the assumption can not be met, for example, the correlation between independent variables (multicollinearity), constraints on the number of data and independent variables are obtained. When the number of samples obtained less than the number of independent variables, then the data is called the microarray data. Least Absolute shrinkage and Selection Operator (LASSO) and Partial Least Squares (PLS) is a statistical method that can be used to overcome the microarray, overfitting, and multicollinearity. From the above description, it is necessary to study with the intention of comparing LASSO and PLS method. This study uses coronary heart and stroke patients data which is a microarray data and contain multicollinearity. With these two characteristics of the data that most have a weak correlation between independent variables, LASSO method produces a better model than PLS seen from the large RMSEP.

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Author Biographies

KADEK DWI FARMANI, Universitas Udayana
Jurusan Matematika, Fakultas MIPA
I PUTU EKA NILA KENCANA, Universitas Udayana
Jurusan Matematika, Fakultas MIPA
KOMANG GDE SUKARSA, Universitas Udayana
JUrusan Matematika, Fakultas MIPA
Published
2012-09-16
How to Cite
FARMANI, KADEK DWI; NILA KENCANA, I PUTU EKA; SUKARSA, KOMANG GDE. PERBANDINGAN ANALISIS LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR DAN PARTIAL LEAST SQUARES (Studi Kasus: Data Microarray). E-Jurnal Matematika, [S.l.], sep. 2012. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/1786>. Date accessed: 03 june 2020. doi: https://doi.org/10.24843/MTK.2012.v01.i01.p013.
Section
Articles

Keywords

microarray; overfitting; RMSEP; LASSO; PLS

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