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In the literature, different methods to obtain robust estimate of location and covariance matrix are available. One of the most popular and widely used is the so-called fast minimum covariance determinant (FMCD). However, it is computationally not efficient when the number of variables is large beca...
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id-itb.:112212017-09-27T11:43:06Z#TITLE_ALTERNATIVE# TISHA DESTRIA (NIM: 10104034), RATU Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/11221 In the literature, different methods to obtain robust estimate of location and covariance matrix are available. One of the most popular and widely used is the so-called fast minimum covariance determinant (FMCD). However, it is computationally not efficient when the number of variables is large because it depends on the computation of the determinant and inverse of covariance matrix. To handle this obstacle, in this final project a study literature on FMCD and a simulation experiment on RH method proposed by our supervisor will be presented. Simulation result shows that RH method is more efficient than FMCD. <br /> <br /> text |
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In the literature, different methods to obtain robust estimate of location and covariance matrix are available. One of the most popular and widely used is the so-called fast minimum covariance determinant (FMCD). However, it is computationally not efficient when the number of variables is large because it depends on the computation of the determinant and inverse of covariance matrix. To handle this obstacle, in this final project a study literature on FMCD and a simulation experiment on RH method proposed by our supervisor will be presented. Simulation result shows that RH method is more efficient than FMCD. <br />
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Final Project |
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TISHA DESTRIA (NIM: 10104034), RATU |
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TISHA DESTRIA (NIM: 10104034), RATU #TITLE_ALTERNATIVE# |
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TISHA DESTRIA (NIM: 10104034), RATU |
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TISHA DESTRIA (NIM: 10104034), RATU |
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https://digilib.itb.ac.id/gdl/view/11221 |
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