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Positive graphical lasso estimation of sparse inverse covariance matrices Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_projects/2227mr28q

2016

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  • We explore the possibility of estimating sparse inverse covariance matrices when for scientific reasons the covariance matrix is restricted to be a non-negative matrix. The process mirrors the graphical lasso process developed by Friedman and others(2008) that did not have this additional constraint.Accordingly, the Lasso procedure is done through coordinate descent. To easily add the constraint, we modified the LARS function created by Efron and others(2004) to perform positive Lasso (pLasso) estimation. The process is demonstrated on several time series generated datasets to clearly show the effectiveness and limitations.
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