Statistical summaries of unlabelled evolutionary trees
Biometrika. 2023 Apr 26;111(1):171-193. doi: 10.1093/biomet/asad025. eCollection 2024 Mar.ABSTRACTRooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, rela...
Source: Biometrika - February 14, 2024 Category: Biotechnology Authors: Rajanala Samyak Julia A Palacios Source Type: research
Statistical summaries of unlabelled evolutionary trees
Biometrika. 2023 Apr 26;111(1):171-193. doi: 10.1093/biomet/asad025. eCollection 2024 Mar.ABSTRACTRooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, rela...
Source: Biometrika - February 14, 2024 Category: Biotechnology Authors: Rajanala Samyak Julia A Palacios Source Type: research
Statistical summaries of unlabelled evolutionary trees
Biometrika. 2023 Apr 26;111(1):171-193. doi: 10.1093/biomet/asad025. eCollection 2024 Mar.ABSTRACTRooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, rela...
Source: Biometrika - February 14, 2024 Category: Biotechnology Authors: Rajanala Samyak Julia A Palacios 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
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
