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On Sparse Nonparametric Conditional Covariance Selection

On Sparse Nonparametric Conditional Covariance Selection,Mladen Kolar,Ankur P. Parikh,Eric P. Xing

On Sparse Nonparametric Conditional Covariance Selection   (Citations: 1)
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We develop a penalized kernel smoothing method for the problem of selecting non- zero elements of the conditional precision ma- trix, known as conditional covariance selec- tion. This problem has a key role in many modern applications such as finance and com- putational biology. However, it has not been properly addressed. Our estimator is derived under minimal assumptions on the underly- ing probability distribution and works well in the high-dimensional setting. The effi- ciency of the algorithm is demonstrated on both simulation studies and the analysis of the stock market.
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