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<http://digilib.unila.ac.id/id/eprint/30988> <http://purl.org/dc/terms/title> "ANALISIS REGRESI DENGAN METODE LEAST ABSOLUTE\r\nSHRINKAGE AND SELECTION OPERATOR (LASSO)\r\nDALAM MENANGANI MULTIKOLINEARITAS\r\n"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://digilib.unila.ac.id/id/eprint/30988> <http://purl.org/ontology/bibo/abstract> "LASSO merupakan metode yang dapat mengatasi masalah multikolinearitas dengan menyusutkan beberapa koefisien regresi mendekati nol dan bahkan menjadi tepat nol.  Dengan algoritma LARS perhitungan LASSO menjadi lebih efisien dibandingkan pemrograman kuadratik.  Tujuan dari penelitian ini adalah untuk mengetahui performa metode LASSO dalam mengatasi multikolinearitas dan membandingkan nilai dugaannya dengan Metode Kuadrat Terkecil.  Hasil menunjukkan bahwa metode LASSO tidak dapat  mengatasi multikolinearitas ketika seluruh variabel bebas berkorelasi tinggi.  Namun, LASSO menangani multikolinearitas dengan baik ketika korelasi terjadi hanya pada sebagian variabel bebas.  Pada jumlah data yang lebih besar, LASSO menghasilkan dugaan koefisien regresi yang lebih baik daripada MKT.  Hal tersebut juga dapat terlihat pada data real.\r\n\r\n\r\nKata Kunci: LASSO, LARS, multikolinearitas\r\n\r\n\r\nABSTRACT\r\n\r\n\r\nLASSO can solve multicolinearity by shrinkage some coefficients to zero or exactly zero.  With LARS algorithm the calculation of LASSO is more efficient than quadratic programming.  The purpose of this study is to know the performance of LASSO to handle multicolinearity and compare its etimates with OLS.  The results show that LASSO can not solve the multicolinearity when all of the independent variables are correlated.  However, LASSO solve the multicolinearity when the correlation partially exist in the independent variables.  In higher sample sizes, LASSO gives regression coefficients estimation better than OLS.  This behavior can also be seen in the real data.\r\n\r\n\r\nKeywords: LASSO, LARS, multicolinearity\r\n"^^<http://www.w3.org/2001/XMLSchema#string> .
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