cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1473747.v2
title: Efficient Quantile Regression Analysis With Missing Observations

description: This article examines the problem of estimation in a quantile regression model when observations are missing at random under independent and nonidentically distributed errors. We consider three approaches of handling this problem based on nonparametric inverse probability weighting, estimating equations projection, and a combination of both. An important distinguishing feature of our methods is their ability to handle missing response and/or partially missing covariates, whereas existing techniques can handle only one or the other, but not both. We prove that our methods yield asymptotically equivalent estimators that achieve the desirable asymptotic properties of unbiasedness, normality, and n-consistency. Because we do not assume that the errors are identically distributed, our theoretical results are valid under heteroscedasticity, a particularly strong feature of our methods. Under the special case of identical error distributions, all of our proposed estimators achieve the semiparametric efficiency bound. To facilitate the practical implementation of these methods, we develop an iterative method based on the majorize/minimize algorithm for computing the quantile regression estimates, and a bootstrap method for computing their variances. Our simulation findings suggest that all three methods have good finite sample properties. We further illustrate these methods by a real data example. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473753
title: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

description: This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of <i>O</i>(<i>n</i><sup>κ</sup>) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473753.v1
title: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

description: This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of <i>O</i>(<i>n</i><sup>κ</sup>) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473753.v2
title: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

description: This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of <i>O</i>(<i>n</i><sup>κ</sup>) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473753.v3
title: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

description: This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of <i>O</i>(<i>n</i><sup>κ</sup>) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473753.v4
title: Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

description: This work is concerned with marginal sure independence feature screening for ultrahigh dimensional discriminant analysis. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening. In this article, we propose a marginal feature screening procedure based on empirical conditional distribution function. We establish the sure screening and ranking consistency properties for the proposed procedure without assuming any moment condition on the predictors. The proposed procedure enjoys several appealing merits. First, it is model-free in that its implementation does not require specification of a regression model. Second, it is robust to heavy-tailed distributions of predictors and the presence of potential outliers. Third, it allows the categorical response having a diverging number of classes in the order of <i>O</i>(<i>n</i><sup>κ</sup>) with some κ ⩾ 0. We assess the finite sample property of the proposed procedure by Monte Carlo simulation studies and numerical comparison. We further illustrate the proposed methodology by empirical analyses of two real-life datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1473754
title: Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

description: <i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.
DOI: 10.6084/m9.figshare.1473754.v1
title: Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

description: <i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.
DOI: 10.6084/m9.figshare.1473754.v2
title: Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

description: <i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.
DOI: 10.6084/m9.figshare.1473754.v4
title: Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

description: <i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.
DOI: 10.6084/m9.figshare.1473754.v3
title: Nonparametric <i>K</i>-Sample Tests via Dynamic Slicing

description: <i>K</i>-sample testing problems arise in many scientific applications and have attracted statisticians’ attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka “slicing”) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programming algorithm is developed to determine the optimal slicing scheme. Asymptotic and finite-sample properties such as power and null distribution of the resulting test statistic are studied. We compare the proposed testing method with some existing well-known methods and demonstrate its statistical power through extensive simulation studies as well as a real data example. A dynamic slicing method for the one-sample testing problem is further developed and studied under the same framework. Supplementary materials including technical derivations and proofs are available online.
DOI: 10.6084/m9.figshare.1478686
title: Discover natural compounds as potential phosphodiesterase-4B inhibitors via computational approaches

description: cAMP, intracellular cyclic adenosine monophosphate, is a ubiquitous second messenger that plays a key role in many physiological processes. PDE4B which can reduce the cAMP level by hydrolyzing cAMP to 5′-AMP has become a therapeutic target for the treatment of human diseases such as respiratory disorders, inflammation diseases, neurological and psychiatric disorders. However, the use of currently available PDE4B inhibitors is restricted due to serious side effects caused by targeting PDE4D. Hence, we are attempting to find out subfamily-selective PDE4B inhibitors from natural products, using computer-aided approaches such as virtual screening, docking, and molecular dynamics simulation. Finally, four potential PDE4B-selective inhibitors (ZINC67912770, ZINC67912780, ZINC72320169, and ZINC28882432) were found. Compared to the reference drug (roflumilast), they scored better during the virtual screening process. Binding free energy for them was −317.51, −239.44, −215.52, and −165.77 kJ/mol, better than −129.05 kJ/mol of roflumilast. The pharmacophore model of the four candidate inhibitors comprised six features, including one hydrogen bond donor, four hydrogen bond acceptors, and one aromatic ring feature. It is expected that our study will pave the way for the design of potent PDE4B-selective inhibitors of new drugs to treat a wide variety of diseases such as asthma, COPD, psoriasis, depression, etc.
DOI: 10.6084/m9.figshare.1478686.v1
title: Discover natural compounds as potential phosphodiesterase-4B inhibitors via computational approaches

description: cAMP, intracellular cyclic adenosine monophosphate, is a ubiquitous second messenger that plays a key role in many physiological processes. PDE4B which can reduce the cAMP level by hydrolyzing cAMP to 5′-AMP has become a therapeutic target for the treatment of human diseases such as respiratory disorders, inflammation diseases, neurological and psychiatric disorders. However, the use of currently available PDE4B inhibitors is restricted due to serious side effects caused by targeting PDE4D. Hence, we are attempting to find out subfamily-selective PDE4B inhibitors from natural products, using computer-aided approaches such as virtual screening, docking, and molecular dynamics simulation. Finally, four potential PDE4B-selective inhibitors (ZINC67912770, ZINC67912780, ZINC72320169, and ZINC28882432) were found. Compared to the reference drug (roflumilast), they scored better during the virtual screening process. Binding free energy for them was −317.51, −239.44, −215.52, and −165.77 kJ/mol, better than −129.05 kJ/mol of roflumilast. The pharmacophore model of the four candidate inhibitors comprised six features, including one hydrogen bond donor, four hydrogen bond acceptors, and one aromatic ring feature. It is expected that our study will pave the way for the design of potent PDE4B-selective inhibitors of new drugs to treat a wide variety of diseases such as asthma, COPD, psoriasis, depression, etc.
DOI: 10.6084/m9.figshare.1478758
title: Timing, petrogenesis, and setting of granites from the southern Beishan late Palaeozoic granitic belt, Northwest China and implications for their tectonic evolution

description: Late Palaeozoic granites are widely distributed in the southeastern Beishan area, which is located in the central part of the southern Central Asian Orogenic Belt (CAOB). U–Pb zircon dates of five late Palaeozoic granitic plutons from the southeastern Beishan area yield Permian ages: 285 ± 2 Ma (Shuwojing and Western Shuwojing plutons), 269 ± 3 Ma (Jianquanzi and Jiuquandihongshan plutons), and 260 ± 1 Ma (Jiujing pluton). The early Permian Shuwojing pluton, an unfractionated calc-alkaline biotite monzogranite with slightly positive εNd(<i>t</i>) (+0.7 and +0.6) and low initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70722 and 0.70712), and the early Permian Western Shuwojing pluton, a high-K calc-alkaline biotite granite with slightly negative εNd(<i>t</i>) (−0.2 and −0.1) and low initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70390 and 0.70919), are likely derived from a mixture of depleted (juvenile) mantle and upper continental crustal (or sedimentary) material. The mid-Permian Jianquanzi and Jiuquandihongshan monzogranites have highly fractionated potassium-rich calc-alkaline characteristics with negative εNd(<i>t</i>) (−4.3) and very high initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.71949), reflecting a continental crustal component. The compositionally diverse Jiujing pluton and the granodiorite and high-Sr monzogranite phases display adakite-like compositions with relatively low εNd(<i>t</i>) (−0.1 and −2.2) and high initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70822 and 0.70913). The Jiujing low-Sr monzogranite has higher initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.73464) and lower εNd(<i>t</i>) (−2.8), indicating a significant continental crustal component in its genesis. This work, combined with the regional geology and previous studies, suggest that the early to middle Permian southern Beishan plutons formed in a post-collisional environment, probably an intracontinental rift environment linked to asthenospheric upwelling in response to the break-off of a subducted slab. In the late Permian, the southern Beishan area was in a compressive tectonic regime and thickening of the continental crust resulted in the formation of the Jiujing adakite-like granite.
DOI: 10.6084/m9.figshare.1478758.v1
title: Timing, petrogenesis, and setting of granites from the southern Beishan late Palaeozoic granitic belt, Northwest China and implications for their tectonic evolution

description: Late Palaeozoic granites are widely distributed in the southeastern Beishan area, which is located in the central part of the southern Central Asian Orogenic Belt (CAOB). U–Pb zircon dates of five late Palaeozoic granitic plutons from the southeastern Beishan area yield Permian ages: 285 ± 2 Ma (Shuwojing and Western Shuwojing plutons), 269 ± 3 Ma (Jianquanzi and Jiuquandihongshan plutons), and 260 ± 1 Ma (Jiujing pluton). The early Permian Shuwojing pluton, an unfractionated calc-alkaline biotite monzogranite with slightly positive εNd(<i>t</i>) (+0.7 and +0.6) and low initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70722 and 0.70712), and the early Permian Western Shuwojing pluton, a high-K calc-alkaline biotite granite with slightly negative εNd(<i>t</i>) (−0.2 and −0.1) and low initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70390 and 0.70919), are likely derived from a mixture of depleted (juvenile) mantle and upper continental crustal (or sedimentary) material. The mid-Permian Jianquanzi and Jiuquandihongshan monzogranites have highly fractionated potassium-rich calc-alkaline characteristics with negative εNd(<i>t</i>) (−4.3) and very high initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.71949), reflecting a continental crustal component. The compositionally diverse Jiujing pluton and the granodiorite and high-Sr monzogranite phases display adakite-like compositions with relatively low εNd(<i>t</i>) (−0.1 and −2.2) and high initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.70822 and 0.70913). The Jiujing low-Sr monzogranite has higher initial <sup>87</sup>Sr/<sup>86</sup>Sr (0.73464) and lower εNd(<i>t</i>) (−2.8), indicating a significant continental crustal component in its genesis. This work, combined with the regional geology and previous studies, suggest that the early to middle Permian southern Beishan plutons formed in a post-collisional environment, probably an intracontinental rift environment linked to asthenospheric upwelling in response to the break-off of a subducted slab. In the late Permian, the southern Beishan area was in a compressive tectonic regime and thickening of the continental crust resulted in the formation of the Jiujing adakite-like granite.