Precision Matrix Estimation With ROPE
|Author:||Kuismin, M.O.1; Kemppainen, J. T.1; Sillanpää, M. J.2|
1Department of Mathematical Sciences, University of Oulu, Oulu, Finland
2Department of Mathematical Sciences, Biocenter Oulu, University of Oulu, Oulu, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201709208664
Taylor & Francis,
|Publish Date:|| 2018-01-08
It is known that the accuracy of the maximum likelihood-based covariance and precision matrix estimates can be improved by penalized log-likelihood estimation. In this article, we propose a ridge-type operator for the precision matrix estimation, ROPE for short, to maximize a penalized likelihood function where the Frobenius norm is used as the penalty function. We show that there is an explicit closed form representation of a shrinkage estimator for the precision matrix when using a penalized log-likelihood, which is analogous to ridge regression in a regression context. The performance of the proposed method is illustrated by a simulation study and real data applications. Computer code used in the example analyses as well as other supplementary materials for this article are available online.
Journal of computational and graphical statistics
|Pages:||682 - 694|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
This work was supported by the University of Oulu’s Exactus Doctoral Programme.
©2017 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 8 Jan 2017, available online: http://www.tandfonline.com/10.1080/10618600.2016.1278002