Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Science and Mathematics Education
Hameedah Naeem Melik
Unknown Affiliation
Education Mathematics Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Lasso Tobit Principal Component Regression With An Application Hameedah Naeem Melik
International Journal of Science and Mathematics Education Vol. 1 No. 4 (2024): December: International Journal of Science and Mathematics Education
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijsme.v1i4.32

Abstract

One of the most crucial subjects in the analysis of statistical models  is the identification of important variables. Therefore, the search for best variable selection methods is a good in obtaining best estimators. The Lasso method is considered the most effective approach for variable selection and parameter estimation in building statistical models with high explanatory power in representing the studied phenomenon. Therefore, using the Lasso method to estimate the parameters of a regression model that contains a dependent variable with data that is censored at zero can be achieved through the use of    Lasso tobit principal component regression, it has attractive properties in estimating the parameters of this model. The our proposed method is illustrated via simulation scenario and a new real data .
Co-Authors