Joint estimation of multiple graphical models
نویسندگان
چکیده
منابع مشابه
Joint estimation of multiple graphical models.
Gaussian graphical models explore dependence relationships between random variables, through the estimation of the corresponding inverse covariance matrices. In this paper we develop an estimator for such models appropriate for data from several graphical models that share the same variables and some of the dependence structure. In this setting, estimating a single graphical model would mask th...
متن کاملJoint Structural Estimation of Multiple Graphical Models
Gaussian graphical models capture dependence relationships between random variables through the pattern of nonzero elements in the corresponding inverse covariance matrices. To date, there has been a large body of literature on both computational methods and analytical results on the estimation of a single graphical model. However, in many application domains, one has to estimate several relate...
متن کاملJoint estimation of sparse multivariate regression and conditional graphical models
Multivariate regression model is a natural generalization of the classical univariate regression model for fitting multiple responses. In this paper, we propose a highdimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency structure among the multiple responses. The proposed method...
متن کاملJoint estimation of multiple dependent Gaussian graphical models with applications to mouse genomics
Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissues from the same individual: here the multivariate outcome is affected by dep...
متن کاملJoint Estimation of Multiple Graphical Models from High Dimensional Time Series.
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for j...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrika
سال: 2011
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/asq060