Statistics – Methodology
Scientific paper
2009-03-31
Statistics and Computing, 2012, Volume 22, 219-235
Statistics
Methodology
The final publication is available at http://www.springerlink.com
Scientific paper
10.1007/s11222-010-9219-7
We propose an l1-regularized likelihood method for estimating the inverse covariance matrix in the high-dimensional multivariate normal model in presence of missing data. Our method is based on the assumption that the data are missing at random (MAR) which entails also the completely missing at random case. The implementation of the method is non-trivial as the observed negative log-likelihood generally is a complicated and non-convex function. We propose an efficient EM algorithm for optimization with provable numerical convergence properties. Furthermore, we extend the methodology to handle missing values in a sparse regression context. We demonstrate both methods on simulated and real data.
Bühlmann Peter
Städler Nicolas
No associations
LandOfFree
Missing values: sparse inverse covariance estimation and an extension to sparse regression does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Missing values: sparse inverse covariance estimation and an extension to sparse regression, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Missing values: sparse inverse covariance estimation and an extension to sparse regression will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-20649