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kjetil b halvorsen ♦
kjetil b halvorsen ♦
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Finding the covariance matrix that fits the data and has a conveniently large number of zero entries in it's inverse matrix is known as Covariance Selection (1). Zeros Zeros in the inverse covariance matrix are desirable both for computational and conceptual reasons: they indicate conditional independence between variables, making the model smaller.

Covariance selection is an active field of research with applications in domains from proteomics to economics. Several alogithmsalgorithms have been proposed and implementations are available in statistical software, for example glasso and smac in R. This presentation (2) provides a really nice overview.

Setting small values to zero does the same thing, but the glasso algorithm finds more zeros. For my particular problem of only 9 variables i could change 3 (of 36) connections to 0, while glasso found 9.

  1. Dempster, A. P. Covariance Selection Biometrics, 1972, 28, 157-175

  2. P. Olsen, F. Oztoprak, J. Nocedal and S. Rennie; Sparse Inverse Covariance Estimation; Summer Tutorial at IBM TJ Watson Research Center 2012

Finding the covariance matrix that fits the data and has a conveniently large number of zero entries in it's inverse matrix is known as Covariance Selection (1). Zeros in the inverse covariance matrix are desirable both for computational and conceptual reasons: they indicate conditional independence between variables, making the model smaller.

Covariance selection is an active field of research with applications in domains from proteomics to economics. Several alogithms have been proposed and implementations are available in statistical software, for example glasso and smac in R. This presentation (2) provides a really nice overview.

Setting small values to zero does the same thing, but the glasso algorithm finds more zeros. For my particular problem of only 9 variables i could change 3 (of 36) connections to 0, while glasso found 9.

  1. Dempster, A. P. Covariance Selection Biometrics, 1972, 28, 157-175

  2. P. Olsen, F. Oztoprak, J. Nocedal and S. Rennie; Sparse Inverse Covariance Estimation; Summer Tutorial at IBM TJ Watson Research Center 2012

Finding the covariance matrix that fits the data and has a conveniently large number of zero entries in it's inverse matrix is known as Covariance Selection (1). Zeros in the inverse covariance matrix are desirable both for computational and conceptual reasons: they indicate conditional independence between variables, making the model smaller.

Covariance selection is an active field of research with applications in domains from proteomics to economics. Several algorithms have been proposed and implementations are available in statistical software, for example glasso and smac in R. This presentation (2) provides a really nice overview.

Setting small values to zero does the same thing, but the glasso algorithm finds more zeros. For my particular problem of only 9 variables i could change 3 (of 36) connections to 0, while glasso found 9.

  1. Dempster, A. P. Covariance Selection Biometrics, 1972, 28, 157-175

  2. P. Olsen, F. Oztoprak, J. Nocedal and S. Rennie; Sparse Inverse Covariance Estimation; Summer Tutorial at IBM TJ Watson Research Center 2012

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Ivana
Ivana
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Finding the covariance matrix that fits the data and has a conveniently large number of zero entries in it's inverse matrix is known as Covariance Selection (1). Zeros in the inverse covariance matrix are desirable both for computational and conceptual reasons: they indicate conditional independence between variables, making the model smaller.

Covariance selection is an active field of research with applications in domains from proteomics to economics. Several alogithms have been proposed and implementations are available in statistical software, for example glasso and smac in R. This presentation (2) provides a really nice overview.

Setting small values to zero does the same thing, but the glasso algorithm finds more zeros. For my particular problem of only 9 variables i could change 3 (of 36) connections to 0, while glasso found 9.

  1. Dempster, A. P. Covariance Selection Biometrics, 1972, 28, 157-175

  2. P. Olsen, F. Oztoprak, J. Nocedal and S. Rennie; Sparse Inverse Covariance Estimation; Summer Tutorial at IBM TJ Watson Research Center 2012