cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1172368
title: ConvexLAR: An Extension of Least Angle Regression

description: The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani in the year 2004 for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way. We propose a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection. After simple modification, it also yields new exact path algorithms for certain penalty methods such as a convex loss function with lasso or group lasso penalty. Variable selection in recurrent event and panel count data analysis, Ada-Boost, and Gaussian graphical model is reconsidered from the ConvexLAR angle. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1172368.v1
title: ConvexLAR: An Extension of Least Angle Regression

description: The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani (2004) for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way. We propose a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection. After simple modification it also yields new exact path algorithms for certain penalty methods such as a convex loss function with lasso or group lasso penalty. Variable selection in recurrent event and panel count data analysis, Ada-Boost, and Gaussian graphical model is reconsidered from the ConvexLAR angle.
DOI: 10.6084/m9.figshare.1172368.v3
title: ConvexLAR: An Extension of Least Angle Regression

description: The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani in the year 2004 for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way. We propose a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection. After simple modification, it also yields new exact path algorithms for certain penalty methods such as a convex loss function with lasso or group lasso penalty. Variable selection in recurrent event and panel count data analysis, Ada-Boost, and Gaussian graphical model is reconsidered from the ConvexLAR angle. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1172368.v2
title: ConvexLAR: An Extension of Least Angle Regression

description: The least angle regression (LAR) was proposed by Efron, Hastie, Johnstone and Tibshirani in the year 2004 for continuous model selection in linear regression. It is motivated by a geometric argument and tracks a path along which the predictors enter successively and the active predictors always maintain the same absolute correlation (angle) with the residual vector. Although it gains popularity quickly, its extensions seem rare compared to the penalty methods. In this expository article, we show that the powerful geometric idea of LAR can be generalized in a fruitful way. We propose a ConvexLAR algorithm that works for any convex loss function and naturally extends to group selection and data adaptive variable selection. After simple modification, it also yields new exact path algorithms for certain penalty methods such as a convex loss function with lasso or group lasso penalty. Variable selection in recurrent event and panel count data analysis, Ada-Boost, and Gaussian graphical model is reconsidered from the ConvexLAR angle. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1177974
title: Is the Bayan Obo ore deposit a micrite mound? A comparison with the Sailinhudong micrite mound

description: The origin of the Bayan Obo ore deposit, the largest REE deposit in the world, has long been debated and various hypotheses have been proposed. Among them is that the Bayan Obo ore deposit is correlated with and has the same origin as the Sailinhudong micrite mound in the southern limb of the Bayan Obo synclinorium. To test this model, the Bayan Obo ore deposit and the Sailinhudong micrite mound are systematically compared for their geological features, elemental geochemistry, and C, O, and Mg isotopic geochemistry. We show that the Bayan Obo ore deposit and the Sailinhudong micrite mound are both calcareous, lens-like in shape, lack bedding features, and are both hosted in a sedimentary formation that consists of clastic sediments and carbonates, unconformably overlying the Archaean–Palaeoproterozoic crystalline basement. However, their geochemical characteristics differ markedly. Compared with the Sailinhudong micrite carbonates, the Bayan Obo ore-hosting dolomite marbles are strongly enriched in LREEs, Ba, Th, Nb, Pb, and Sr, and have very different (PAAS)-normalized REE patterns. Sailinhudong micrite carbonates have higher δ<sup>13</sup>C<sub>PDB</sub> and δ<sup>18</sup>O<sub>SMOW</sub> values, falling into the typical sedimentary field, but the Bayan Obo ore-hosting dolomites are isotopically intermediate between primary igneous carbonatite and typical sedimentary limestone. The δ<sup>26</sup> Mg values of the Sailinhudong micrite carbonates are lighter than those of normal Mesoproterozoic sedimentary dolostone, while those of the Bayan Obo ore-hosting dolomite marble are isotopically heavier, similar to δ<sup>26</sup> Mg of mantle xenoliths and Bayan Obo intrusive carbonatite. We conclude that the Bayan Obo ore deposit is not correlated with the Sailinhudong micrite mound; it is neither a micrite mound nor an altered micrite mound.
DOI: 10.6084/m9.figshare.1177974.v2
title: Is the Bayan Obo ore deposit a micrite mound? A comparison with the Sailinhudong micrite mound

description: The origin of the Bayan Obo ore deposit, the largest REE deposit in the world, has long been debated and various hypotheses have been proposed. Among them is that the Bayan Obo ore deposit is correlated with and has the same origin as the Sailinhudong micrite mound in the southern limb of the Bayan Obo synclinorium. To test this model, the Bayan Obo ore deposit and the Sailinhudong micrite mound are systematically compared for their geological features, elemental geochemistry, and C, O, and Mg isotopic geochemistry. We show that the Bayan Obo ore deposit and the Sailinhudong micrite mound are both calcareous, lens-like in shape, lack bedding features, and are both hosted in a sedimentary formation that consists of clastic sediments and carbonates, unconformably overlying the Archaean–Palaeoproterozoic crystalline basement. However, their geochemical characteristics differ markedly. Compared with the Sailinhudong micrite carbonates, the Bayan Obo ore-hosting dolomite marbles are strongly enriched in LREEs, Ba, Th, Nb, Pb, and Sr, and have very different (PAAS)-normalized REE patterns. Sailinhudong micrite carbonates have higher δ<sup>13</sup>C<sub>PDB</sub> and δ<sup>18</sup>O<sub>SMOW</sub> values, falling into the typical sedimentary field, but the Bayan Obo ore-hosting dolomites are isotopically intermediate between primary igneous carbonatite and typical sedimentary limestone. The δ<sup>26</sup> Mg values of the Sailinhudong micrite carbonates are lighter than those of normal Mesoproterozoic sedimentary dolostone, while those of the Bayan Obo ore-hosting dolomite marble are isotopically heavier, similar to δ<sup>26</sup> Mg of mantle xenoliths and Bayan Obo intrusive carbonatite. We conclude that the Bayan Obo ore deposit is not correlated with the Sailinhudong micrite mound; it is neither a micrite mound nor an altered micrite mound.
DOI: 10.6084/m9.figshare.1177974.v1
title: Is the Bayan Obo ore deposit a micrite mound? A comparison with the Sailinhudong micrite mound

description: The origin of the Bayan Obo ore deposit, the largest REE deposit in the world, has long been debated and various hypotheses have been proposed. Among them is that the Bayan Obo ore deposit is correlated with and has the same origin as the Sailinhudong micrite mound in the southern limb of the Bayan Obo synclinorium. To test this model, the Bayan Obo ore deposit and the Sailinhudong micrite mound are systematically compared for their geological features, elemental geochemistry, and C, O, and Mg isotopic geochemistry. We show that the Bayan Obo ore deposit and the Sailinhudong micrite mound are both calcareous, lens-like in shape, lack bedding features, and are both hosted in a sedimentary formation that consists of clastic sediments and carbonates, unconformably overlying the Archaean–Palaeoproterozoic crystalline basement. However, their geochemical characteristics differ markedly. Compared with the Sailinhudong micrite carbonates, the Bayan Obo ore-hosting dolomite marbles are strongly enriched in LREEs, Ba, Th, Nb, Pb, and Sr, and have very different (PAAS)-normalized REE patterns. Sailinhudong micrite carbonates have higher δ<sup>13</sup>C<sub>PDB</sub> and δ<sup>18</sup>O<sub>SMOW</sub> values, falling into the typical sedimentary field, but the Bayan Obo ore-hosting dolomites are isotopically intermediate between primary igneous carbonatite and typical sedimentary limestone. The δ<sup>26</sup> Mg values of the Sailinhudong micrite carbonates are lighter than those of normal Mesoproterozoic sedimentary dolostone, while those of the Bayan Obo ore-hosting dolomite marble are isotopically heavier, similar to δ<sup>26</sup> Mg of mantle xenoliths and Bayan Obo intrusive carbonatite. We conclude that the Bayan Obo ore deposit is not correlated with the Sailinhudong micrite mound; it is neither a micrite mound nor an altered micrite mound.
DOI: 10.6084/m9.figshare.1189297
title: Self-propagative Replication of Aβ Oligomers Suggests Potential Transmissibility in Alzheimer Disease.

description: These are figures generated for publicaiton in PLoS One
DOI: 10.6084/m9.figshare.1189297.v1
title: Self-propagative Replication of Aβ Oligomers Suggests Potential Transmissibility in Alzheimer Disease.

description: These are figures generated for publicaiton in PLoS One
DOI: 10.6084/m9.figshare.1203456
title: A binary approximating method for graspable region determination of biped climbing robots

description: For a biped pole-climbing robot (BiPCR) with grippers, it is an essential demand to determine the target grasp configuration for climbing and transiting between poles, with the graspable region as a priori knowledge. The graspable region on the target pole is critically important for climbing path planning and motion control. To efficiently compute the graspable region for a BiPCR, we propose a novel binary approximating method in this paper. This method may also be applied to generate the three-dimensional (3-D) workspace of a manipulator with constant orientation. The grasping problem and the concept of graspable region for a BiPCR are first introduced. The binary approximating method and the corresponding algorithms are then presented to generate the graspable region. Additional constraints on a biped climbing robot with five degrees of freedom (DoFs) are presented as a supplement to the algorithm. A series of comprehensive simulations are conducted with the five-DoF and six-DoF climbing robots to verify the effectiveness of the proposed method. Finally, the dexterity of biped climbing robots with different DoFs is discussed.
DOI: 10.6084/m9.figshare.1203456.v2
title: A binary approximating method for graspable region determination of biped climbing robots

description: For a biped pole-climbing robot (BiPCR) with grippers, it is an essential demand to determine the target grasp configuration for climbing and transiting between poles, with the graspable region as a priori knowledge. The graspable region on the target pole is critically important for climbing path planning and motion control. To efficiently compute the graspable region for a BiPCR, we propose a novel binary approximating method in this paper. This method may also be applied to generate the three-dimensional (3-D) workspace of a manipulator with constant orientation. The grasping problem and the concept of graspable region for a BiPCR are first introduced. The binary approximating method and the corresponding algorithms are then presented to generate the graspable region. Additional constraints on a biped climbing robot with five degrees of freedom (DoFs) are presented as a supplement to the algorithm. A series of comprehensive simulations are conducted with the five-DoF and six-DoF climbing robots to verify the effectiveness of the proposed method. Finally, the dexterity of biped climbing robots with different DoFs is discussed.
DOI: 10.6084/m9.figshare.1203456.v1
title: A binary approximating method for graspable region determination of biped climbing robots

description: For a biped pole-climbing robot (BiPCR) with grippers, it is an essential demand to determine the target grasp configuration for climbing and transiting between poles, with the graspable region as a priori knowledge. The graspable region on the target pole is critically important for climbing path planning and motion control. To efficiently compute the graspable region for a BiPCR, we propose a novel binary approximating method in this paper. This method may also be applied to generate the three-dimensional (3-D) workspace of a manipulator with constant orientation. The grasping problem and the concept of graspable region for a BiPCR are first introduced. The binary approximating method and the corresponding algorithms are then presented to generate the graspable region. Additional constraints on a biped climbing robot with five degrees of freedom (DoFs) are presented as a supplement to the algorithm. A series of comprehensive simulations are conducted with the five-DoF and six-DoF climbing robots to verify the effectiveness of the proposed method. Finally, the dexterity of biped climbing robots with different DoFs is discussed.
DOI: 10.6084/m9.figshare.1203657
title: Land-use Inventory of Hangzhou (2010)

description: Land-use Inventory of Hangzhou (2010) contains green space,water,build up and bare land.
This data is part of a published article:
Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis
DOI: 10.6084/m9.figshare.1206397
title: Accounting for time series errors in partially linear model with single- or multiple-run

description: This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance experimental accuracy without extending the number of time points within each trial. Such features arise naturally from biomedical data in for e.g. brain fMRI and call for special treatment beyond classical methods in either a purely nonparametric regression model or a PLM with independent errors. We develop a stepwise procedure for estimating the parametric and nonparametric components of the multiple-run PLM and making inference for parameters of interest, adaptive to either single- or multiple-run, in the presence of error temporal dependence. Simulation study and real fMRI data applications illustrate the computational simplicity and effectiveness of the proposed methods. Supplementary material for this paper is available online.
DOI: 10.6084/m9.figshare.1206397.v1
title: Accounting for time series errors in partially linear model with single- or multiple-run

description: This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance experimental accuracy without extending the number of time points within each trial. Such features arise naturally from biomedical data in for e.g. brain fMRI and call for special treatment beyond classical methods in either a purely nonparametric regression model or a PLM with independent errors. We develop a stepwise procedure for estimating the parametric and nonparametric components of the multiple-run PLM and making inference for parameters of interest, adaptive to either single- or multiple-run, in the presence of error temporal dependence. Simulation study and real fMRI data applications illustrate the computational simplicity and effectiveness of the proposed methods. Supplementary material for this paper is available online.
DOI: 10.6084/m9.figshare.1209695
title: Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models

description: In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods. It is shown that the popular <i>K</i>-fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online.
DOI: 10.6084/m9.figshare.1209695.v1
title: Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models

description: In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods. It is shown that the popular <i>K</i>-fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online.
DOI: 10.6084/m9.figshare.1209695.v2
title: Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models

description: In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods. It is shown that the popular <i>K</i>-fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online.
DOI: 10.6084/m9.figshare.1209695.v3
title: Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models

description: In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. We conduct extensive simulation studies and real data analysis to compare the performance of the modified CV method with other methods. It is shown that the popular <i>K</i>-fold CV method includes many noise variables in the selected model, while the modified CV works well in a wide range of coefficient and correlation settings. Supplementary materials containing the computer code are available online.
DOI: 10.6084/m9.figshare.1209702
title: Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

description: Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.
DOI: 10.6084/m9.figshare.1209702.v1
title: Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

description: Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.
DOI: 10.6084/m9.figshare.1209702.v2
title: Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

description: Many statistical methods gain robustness and flexibility by sacrificing convenient computational structures. In this article, we illustrate this fundamental tradeoff by studying a semiparametric graph estimation problem in high dimensions. We explain how novel computational techniques help to solve this type of problem. In particular, we propose a nonparanormal neighborhood pursuit algorithm to estimate high-dimensional semiparametric graphical models with theoretical guarantees. Moreover, we provide an alternative view to analyze the tradeoff between computational efficiency and statistical error under a smoothing optimization framework. Though this article focuses on the problem of graph estimation, the proposed methodology is widely applicable to other problems with similar structures. We also report thorough experimental results on text, stock, and genomic datasets.