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Rahardiantoro, Septian
2Department Of Statistics, Faculty Of Mathematics And Natural Science, IPB University, West Java, 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Mathematics Physics Other

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Journal : FORUM STATISTIKA DAN KOMPUTASI

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LAD-LASSO: SIMULATION STUDY OF ROBUST REGRESSION IN HIGH DIMENSIONAL DATA Septian Rahardiantoro; Anang Kurnia
FORUM STATISTIKA DAN KOMPUTASI Vol. 20 No. 2 (2015)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The common issues in regression, there are a lot of cases in the condition number of predictor variables more than number of observations ( ) called high dimensional data. The classical problem always lies in this case, that is multicolinearity. It would be worse when the datasets subject to heavy-tailed errors or outliers that may appear in the responses and/or the predictors. As this reason, Wang et al in 2007 developed combined methods from Least Absolute Deviation (LAD) regression that is useful for robust regression, and also LASSO that is popular choice for shrinkage estimation and variable selection, becoming LAD-LASSO. Extensive simulation studies demonstrate satisfactory using LAD-LASSO in high dimensional datasets that lies outliers better than using LASSO.Keywords: high dimensional data, LAD-LASSO, robust regression
Co-Authors . Hanniva Aam Alamudi Ade Gusalinda Agus M Soleh Agus Mohamad Soleh Agus Soleh Ahmad Ansori Mattjik Anang Kurnia Anik Djuraidah Astri Fatimah Azira Irawan Azizah Desiwari Bagus Sartono Cici Suhaeni Dito, Gerry Alfa Eria, Raisa Nida I Made Sumertajaya Itasia Dina Sulvianti Khairil Anwar Notodiputro Lutfiah Adisti, Tiara Mahdiyah Riaesnianda Marshelle, Sean Mohammad Masjkur Muhammad Shalih Mutaqin, Ahdan Darul Octavia Dwi Amelia Pika Silvianti Pika Silvianti Radinda Putri Maha Dewi Ramadhani, Faadiyah Resti Cahyati Rizki, Akbar Stephan Chen Totong Martono Yani Nurhadryani Yanti, Yusma Yanti, Yusma Yusma Yanti