Convex Banding of the Covariance Matrix
نویسندگان
چکیده
منابع مشابه
Convex Banding of the Covariance Matrix.
We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding es...
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Abstract Regularization has become a primary tool for developing reliable estimators of the covariance matrix in high-dimensional settings. To curb the curse of dimensionality, numerous methods assume that the population covariance (or inverse covariance) matrix is sparse, while making no particular structural assumptions on the desired pattern of sparsity. A highly-related, yet complementary, ...
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15 صفحه اولCOVARIANCE MATRIX OF MULTIVARIATE REWARD PROCESSES WITH NONLINEAR REWARD FUNCTIONS
Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...
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Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical mode...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2016
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2015.1058265