The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on grap...The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.展开更多
Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential t...Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.展开更多
In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precis...In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precision matrix, we propose Bayesian Lasso together with neighborhood regression estimate for Gaussian graphical model. This method can obtain parameter estimation and model selection simultaneously. Moreover, the proposed method can provide symmetric confidence intervals of all entries of the precision matrix.展开更多
A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ra...A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance,makes standard Gaussian graphical models(GGMs) unsuitable.We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored.The new method,called matrix-variate Gaussian graphical models(MGGMs),involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution.The computation is carried out using a Markov chain Monte Carlo(MCMC) sampling scheme for graphical model determination and parameter estimation.Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.展开更多
Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term f...Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need.Consequently,the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke.Stroke was induced via permanent middle cerebral artery occlusion.At 24 hours post-stroke,MRI analysis revealed focal ischemic lesions,decreased diffusivity,hemispheric swelling,and white matter degradation.Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke.Gaussian graphical models identified specific MRI outputs and functional recovery variables,including white matter integrity and gait performance,that exhibited strong conditional dependencies.Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance.Consequently,these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities(e.g.,white matter composition)that have proven to be critical in ischemic stroke pathophysiology.The study was approved by the University of Georgia(UGA)Institutional Animal Care and Use Committee(IACUC;Protocol Number:A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5)on November 22,2017.展开更多
This paper focuses on the support recovery of the Gaussian graphical model(GGM)with false discovery rate(FDR)control.The graceful symmetrized data aggregation(SDA)technique which involves sample splitting,data screeni...This paper focuses on the support recovery of the Gaussian graphical model(GGM)with false discovery rate(FDR)control.The graceful symmetrized data aggregation(SDA)technique which involves sample splitting,data screening and information pooling is exploited via a node-based way.A matrix of test statistics with symmetry property is constructed and a data-driven threshold is chosen to control the FDR for the support recovery of GGM.The proposed method is shown to control the FDR asymptotically under some mild conditions.Extensive simulation studies and a real-data example demonstrate that it yields a better FDR control while offering reasonable power in most cases.展开更多
目的·探索早年创伤的不同维度与精神分裂症阳性和阴性症状的相关性。方法·在上海市精神卫生中心招募124例精神分裂症患者,采用早年创伤问卷简表(Early Trauma Inventory Short Form,ETI-SF)评估早年创伤情况,采用阳性和阴性...目的·探索早年创伤的不同维度与精神分裂症阳性和阴性症状的相关性。方法·在上海市精神卫生中心招募124例精神分裂症患者,采用早年创伤问卷简表(Early Trauma Inventory Short Form,ETI-SF)评估早年创伤情况,采用阳性和阴性症状量表(Positive and Negative Syndrome Scale,PANSS)评估精神症状。采用Pearson相关分析及高斯图模型网络分析方法,将早年创伤的4个维度分别与PANSS的3个亚量表及30个条目评分进行相关性分析。结果·控制年龄、性别等影响因素后,早年创伤中躯体虐待(r=0.29,P=0.000)和情感虐待(r=0.21,P=0.024)与精神分裂症阳性症状存在显著的相关性。各个维度的创伤均与阳性症状不同子条目存在相关关系;网络分析也验证了躯体虐待和情感虐待与精神分裂症阳性症状存在较强的相关性;在整个网络图中回避社交节点的网络度中心性值最大。结论·早年创伤与精神分裂症阳性症状关系密切,其中躯体虐待与阳性症状显著相关,主动回避社交在整个网络中起重要的中介作用。展开更多
文摘The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.
基金partially supported by the National Natural Science Foundation of China(Grant No.12171079)the National Key R&D Program of China(Grant No.2020YFA0714102)+1 种基金partially supported by the National Natural Science Foundation of China(Grant No.12101116)the National Key Research and Development Program of China(Grant No.2022YFA1003701)。
文摘Gaussian graphical models(GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate(FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is p times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.
基金Supported by the National Natural Science Foundation of China(No.11571080)
文摘In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precision matrix, we propose Bayesian Lasso together with neighborhood regression estimate for Gaussian graphical model. This method can obtain parameter estimation and model selection simultaneously. Moreover, the proposed method can provide symmetric confidence intervals of all entries of the precision matrix.
文摘A new procedure of learning in Gaussian graphical models is proposed under the assumption that samples are possibly dependent.This assumption,which is pragmatically applied in various areas of multivariate analysis ranging from bioinformatics to finance,makes standard Gaussian graphical models(GGMs) unsuitable.We demonstrate that the advantage of modeling dependence among samples is that the true discovery rate and positive predictive value are improved substantially than if standard GGMs are applied and the dependence among samples is ignored.The new method,called matrix-variate Gaussian graphical models(MGGMs),involves simultaneously modeling variable and sample dependencies with the matrix-normal distribution.The computation is carried out using a Markov chain Monte Carlo(MCMC) sampling scheme for graphical model determination and parameter estimation.Simulation studies and two real-world examples in biology and finance further illustrate the benefits of the new models.
基金This work was supported by the National Institutes of Health,National Institute of Neurological Disorders and Stroke grant R01NS093314 as well as Small Business Innovation Research grant 1R43NS103596-01.
文摘Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need.Consequently,the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke.Stroke was induced via permanent middle cerebral artery occlusion.At 24 hours post-stroke,MRI analysis revealed focal ischemic lesions,decreased diffusivity,hemispheric swelling,and white matter degradation.Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke.Gaussian graphical models identified specific MRI outputs and functional recovery variables,including white matter integrity and gait performance,that exhibited strong conditional dependencies.Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance.Consequently,these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities(e.g.,white matter composition)that have proven to be critical in ischemic stroke pathophysiology.The study was approved by the University of Georgia(UGA)Institutional Animal Care and Use Committee(IACUC;Protocol Number:A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5)on November 22,2017.
基金supported partially by the China National Key R&D Program under Grant Nos.2019YFC1908502,2022YFA1003703,2022YFA1003802,and 2022YFA1003803the National Natural Science Foundation of China under Grant Nos.11925106,12231011,11931001,and 11971247。
文摘This paper focuses on the support recovery of the Gaussian graphical model(GGM)with false discovery rate(FDR)control.The graceful symmetrized data aggregation(SDA)technique which involves sample splitting,data screening and information pooling is exploited via a node-based way.A matrix of test statistics with symmetry property is constructed and a data-driven threshold is chosen to control the FDR for the support recovery of GGM.The proposed method is shown to control the FDR asymptotically under some mild conditions.Extensive simulation studies and a real-data example demonstrate that it yields a better FDR control while offering reasonable power in most cases.
文摘目的·探索早年创伤的不同维度与精神分裂症阳性和阴性症状的相关性。方法·在上海市精神卫生中心招募124例精神分裂症患者,采用早年创伤问卷简表(Early Trauma Inventory Short Form,ETI-SF)评估早年创伤情况,采用阳性和阴性症状量表(Positive and Negative Syndrome Scale,PANSS)评估精神症状。采用Pearson相关分析及高斯图模型网络分析方法,将早年创伤的4个维度分别与PANSS的3个亚量表及30个条目评分进行相关性分析。结果·控制年龄、性别等影响因素后,早年创伤中躯体虐待(r=0.29,P=0.000)和情感虐待(r=0.21,P=0.024)与精神分裂症阳性症状存在显著的相关性。各个维度的创伤均与阳性症状不同子条目存在相关关系;网络分析也验证了躯体虐待和情感虐待与精神分裂症阳性症状存在较强的相关性;在整个网络图中回避社交节点的网络度中心性值最大。结论·早年创伤与精神分裂症阳性症状关系密切,其中躯体虐待与阳性症状显著相关,主动回避社交在整个网络中起重要的中介作用。
