Ridge estimation of inverse covariance matrices from high-dimensional data
نویسندگان
چکیده
منابع مشابه
Targeted Fused Ridge Estimation of Inverse Covariance Matrices from Multiple High-Dimensional Data Classes
We consider the problem of jointly estimating multiple precision matrices from (aggregated) high-dimensional data consisting of distinct classes. An `2-penalized maximum-likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other `2-penalized estimators as special cases. In addition, the approach allows for the specification of target matrices th...
متن کاملRegularized Estimation of High-dimensional Covariance Matrices
Regularized Estimation of High-dimensional Covariance Matrices
متن کاملRandom matrix theory and estimation of high-dimensional covariance matrices
This projects aims to present significant results of random matrix theory in regards to the principal component analysis, including Wigner’s semicircular law and Marčenko-Pastur law describing limiting distribution of large dimensional random matrices. The work bases on the large dimensional data assumptions, where both the number of variables and sample size tends to infinity, while their rati...
متن کاملOn the Testing and Estimation of High-Dimensional Covariance Matrices
Many applications of modern science involve a large number of parameters. In many cases, the number of parameters, p, exceeds the number of observations, N . Classical multivariate statistics are based on the assumption that the number of parameters is fixed and the number of observations is large. Many of the classical techniques perform poorly, or are degenerate, in high-dimensional situation...
متن کاملGroup Lasso Estimation of High-dimensional Covariance Matrices
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a highdimensional setting under the assumption that the process has a sparse representation in a large dictionary of basis functions. Using a matrix regression model, we propose a new methodology for high-dimensiona...
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: Computational Statistics & Data Analysis
سال: 2016
ISSN: 0167-9473
DOI: 10.1016/j.csda.2016.05.012