A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
A proximal distance algorithm for likelihood-based sparse covariance estimation
Biometrika. 2022 Dec;109(4):1047-1066. doi: 10.1093/biomet/asac011. Epub 2022 Feb 16.ABSTRACTThis paper addresses the task of estimating a covariance matrix under a patternless sparsity assumption. In contrast to existing approaches based on thresholding or shrinkage penalties, we propose a likelihood-based method that regularizes the distance from the covariance estimate to a symmetric sparsity set. This formulation avoids unwanted shrinkage induced by more common norm penalties, and enables optimization of the resulting nonconvex objective by solving a sequence of smooth, unconstrained subproblems. These subproblems are ...
Source: Biometrika - December 14, 2023 Category: Biotechnology Authors: Jason Xu Kenneth Lange Source Type: research
Discussion of 'Statistical inference for streamed longitudinal data'
Biometrika. 2023 Nov 15;110(4):867-869. doi: 10.1093/biomet/asad043. eCollection 2023 Dec.NO ABSTRACTPMID:37981957 | PMC:PMC10651177 | DOI:10.1093/biomet/asad043 (Source: Biometrika)
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Yang Ning Jingyi Duan Source Type: research
Efficient Estimation under Data Fusion
We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.PMID:37982010 | PMC:PMC10653189 | DOI:10.1093/b...
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Sijia Li Alex Luedtke Source Type: research
Discussion of 'Statistical inference for streamed longitudinal data'
Biometrika. 2023 Nov 15;110(4):867-869. doi: 10.1093/biomet/asad043. eCollection 2023 Dec.NO ABSTRACTPMID:37981957 | PMC:PMC10651177 | DOI:10.1093/biomet/asad043 (Source: Biometrika)
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Yang Ning Jingyi Duan Source Type: research
Efficient Estimation under Data Fusion
We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.PMID:37982010 | PMC:PMC10653189 | DOI:10.1093/b...
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Sijia Li Alex Luedtke Source Type: research
Discussion of 'Statistical inference for streamed longitudinal data'
Biometrika. 2023 Nov 15;110(4):867-869. doi: 10.1093/biomet/asad043. eCollection 2023 Dec.NO ABSTRACTPMID:37981957 | PMC:PMC10651177 | DOI:10.1093/biomet/asad043 (Source: Biometrika)
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Yang Ning Jingyi Duan Source Type: research
Efficient Estimation under Data Fusion
We describe potential gains in efficiency that can arise from fusing these data sources together in a single analysis, which we characterize by a reduction in the semiparametric efficiency bound. We also provide a general means to construct estimators that achieve these bounds. In numerical simulations, we illustrate marked improvements in efficiency from using our proposed estimators rather than their natural alternatives. Finally, we illustrate the magnitude of efficiency gains that can be realized in vaccine immunogenicity studies by fusing data from two HIV vaccine trials.PMID:37982010 | PMC:PMC10653189 | DOI:10.1093/b...
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Sijia Li Alex Luedtke Source Type: research
Discussion of 'Statistical inference for streamed longitudinal data'
Biometrika. 2023 Nov 15;110(4):867-869. doi: 10.1093/biomet/asad043. eCollection 2023 Dec.NO ABSTRACTPMID:37981957 | PMC:PMC10651177 | DOI:10.1093/biomet/asad043 (Source: Biometrika)
Source: Biometrika - November 20, 2023 Category: Biotechnology Authors: Yang Ning Jingyi Duan Source Type: research
