Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo...Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.展开更多
A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used...A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used. Then, a practical appraising index system for nonparametric software reliability models are put forward. The nonparametric software reliability models are classified into 5 classes, that is time series analysis models, grey theo- ry forecasting models, artificial neural network models, wavelet analysis models and kernel estimation models, and they are evaluated by the practical index system.展开更多
In this paper, by using some inequalities of negatively orthant dependent(NOD,in short) random variables and the truncated method of random variables, we investigate the nonparametric regression model. The complete co...In this paper, by using some inequalities of negatively orthant dependent(NOD,in short) random variables and the truncated method of random variables, we investigate the nonparametric regression model. The complete consistency result for the estimator of g(x) is presented.展开更多
It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparame...It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.展开更多
To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 ...To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.展开更多
Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameter...Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior.In this study,a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed.Initially,the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method.Subsequently,a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model.Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty.For example,the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system.The results demonstrate that the influence of uncertainty varies among different model types.Model uncertainties have a more significant than parametric uncertainties,whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses.These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.展开更多
Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is ...Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.展开更多
This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric c...This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.展开更多
In this paper, an exponential inequality for the maximal partial sums of negatively superadditive-dependent （NSD, in short） random variables is established. By uSing the exponen- tial inequality, we present some gen...In this paper, an exponential inequality for the maximal partial sums of negatively superadditive-dependent （NSD, in short） random variables is established. By uSing the exponen- tial inequality, we present some general results on the complete convergence for arrays of rowwise NSD random variables, which improve or generalize the corresponding ones of Wang et al. [28] and Chen et al. [2]. In addition, some sufficient conditions to prove the complete convergence are provided. As an application of the complete convergence that we established, we further investigate the complete consistency and convergence rate of the estimator in a nonparametric regression model based on NSD errors.展开更多
A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empir...A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise. Moreover, the consistence of the test is proved while the rate of convergence is given. The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.展开更多
We establish some results on the complete moment convergence for weighted sums of widely orthant-dependent(WOD)random variables,which improve and extend the corresponding results of Y.F.Wu,M.G.Zhai,and J.Y.Peng[J.Math...We establish some results on the complete moment convergence for weighted sums of widely orthant-dependent(WOD)random variables,which improve and extend the corresponding results of Y.F.Wu,M.G.Zhai,and J.Y.Peng[J.Math.Inequal.,2019,13(1):251–260].As an application of the main results,we investigate the complete consistency for the estimator in a nonparametric regression model based on WOD errors and provide some simulations to verify our theoretical results.展开更多
For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimatin...For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimating f(x) and the consistency of the estimators is obtained.展开更多
Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued, u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is an unknown regression function,which is m...Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued, u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is an unknown regression function,which is m（m≥0）times continuously differentiable and its ruth derivative,g<sub>0</sub><sup>（m）</sup>,satisfies a H■lder condition of order γ（m +γ】1/2）.A piecewise polynomial L<sub>1</sub>- norm estimator of go is proposed.Under some regularity conditions including that the random errors are independent but not necessarily have a common distribution,it is proved that the rates of convergence of the piecewise polynomial L<sub>1</sub>-norm estimator are o(n<sup>-2（m+γ）+1/m+γ-1/δ</sup>almost surely and o(n<sup>-2（m+γ）+1/m+γ-δ</sup>)in probability,which can arbitrarily approach the optimal rates of convergence for nonparametric regression,where δ is any number in （0, min（（m+γ-1/2）/3,γ））.展开更多
Estimation of the extreme conditional quantiles with functional covariate is an important problem in quantile regression. The existing methods, however, are only applicable for heavy-tailed distributions with a positi...Estimation of the extreme conditional quantiles with functional covariate is an important problem in quantile regression. The existing methods, however, are only applicable for heavy-tailed distributions with a positive conditional tail index. In this paper, we propose a new framework for estimating the extreme conditional quantiles with functional covariate that combines the nonparametric modeling techniques and extreme value theory systematically. Our proposed method is widely applicable, no matter whether the conditional distribution of a response variable Y given a vector of functional covariates X is short, light or heavy-tailed. It thus enriches the existing literature.展开更多
Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is...Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.展开更多
This paper studies the income inequality and economic development relationship by using unbalanced panel data of OECD and non-OECD countries(regions)for the period 1962-2003.The nonparametric estimation results show t...This paper studies the income inequality and economic development relationship by using unbalanced panel data of OECD and non-OECD countries(regions)for the period 1962-2003.The nonparametric estimation results show that income inequality in OECD countries is almost on the backside of the inverted-U relationship,while non-OECD countries are approximately on the foreside,except that the relationship in both country groups shows an upturn at a high level of development.Development has an indirect effect on inequality through control variables,but the modes are different in the two country groups.The model specification tests show that the relationship is not necessarily captured by the conventional quadratic function.The cubic and fourthdegree polynomials,respectively,fit the OECD and non-OECD country groups best.Our finding is robust regardless of whether the specification uses control variables.Development plays a dominant role in mitigating inequality.展开更多
This paper presents a measurement of the technical efficiency of textile industries with 4-digit codes in China by using the cross-section data from 2002 and 2007 and a fully nonparametric stochastic frontier estimati...This paper presents a measurement of the technical efficiency of textile industries with 4-digit codes in China by using the cross-section data from 2002 and 2007 and a fully nonparametric stochastic frontier estimation approach. The technical efficiency of these textile industries is compared across six economic ownership types and across seven regions in China. This uncovers the effects of the proprietary characteristics and the location of a firm on its technical efficiency performance. The nonparametric estimation provides some interesting findings. First, textile production in China performs with a decreasing return to scale. The difference between the output elasticity of labor and that of capital decreases from the year 2002 to 2007. Second, the technical efficiency of the 4-digit textile industry in China is significantly contingent on its ownership and location. Privately-run textile enterprises on average perform with the highest level of technical efficiency among the six ownership types while state-owned enterprises perform with the lowest level of technical efficiency, whether or not the location dummies are accounted for. Third, the technical efficiency evaluated by regions follows the order： ＂eastern area 〉 southern area 〉 central area 〉 northern area,＂ which remains unchanged across the two years.展开更多
基金supported by Beijing Natural Science Foundation(No.4142017)International Cooperation Project of National Natural Science Foundation of China(No.61120106009)Beijing Science and Technology Commission Precision Machinery Projects(No.Z121100001612007)
文摘Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.
文摘A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used. Then, a practical appraising index system for nonparametric software reliability models are put forward. The nonparametric software reliability models are classified into 5 classes, that is time series analysis models, grey theo- ry forecasting models, artificial neural network models, wavelet analysis models and kernel estimation models, and they are evaluated by the practical index system.
基金Supported by the Research Teaching Model Curriculum of Anhui University(xjyjkc1407)Supported by the Students Innovative Training Project of Anhui University(201310357004,201410357117,201410357249)Supported by the Quality Improvement Projects for Undergraduate Education of Anhui University(ZLTS2015035)
文摘In this paper, by using some inequalities of negatively orthant dependent(NOD,in short) random variables and the truncated method of random variables, we investigate the nonparametric regression model. The complete consistency result for the estimator of g(x) is presented.
基金supported by the Natural Science Foundation of China(11201345,11271136)
文摘It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.
基金The National Key R&D Program of China under contract No.2016YFC1401004the National Natural Science Foundation of China under contract Nos 41406207,41176157 and 41406197
文摘To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.
基金Supported by National Natural Science Foundation of China(Grant Nos.12072106,52005156)National Key Research and Development Program of China(Grant No.2020YFB2008101)Foundation of Henan Key Laboratory of Superhard Abrasives and Grinding Equipment,Henan University of Technology of China(Grant No.JDKFJJ2022002).
文摘Current research on the dynamics and vibrations of geared rotor systems primarily focuses on deterministic models.However,uncertainties inevitably exist in the gear system,which cause uncertainties in system parameters and subsequently influence the accurate evaluation of system dynamic behavior.In this study,a dynamic model of a geared rotor system with mixed parameters and model uncertainties is proposed.Initially,the dynamic model of the geared rotor-bearing system with deterministic parameters is established using a finite element method.Subsequently,a nonparametric method is introduced to model the hybrid uncertainties in the dynamic model.Deviation coefficients and dispersion parameters are used to reflect the levels of parameter and model uncertainty.For example,the study evaluates the effects of uncertain bearing and mesh stiffness on the vibration responses of a geared rotor system.The results demonstrate that the influence of uncertainty varies among different model types.Model uncertainties have a more significant than parametric uncertainties,whereas hybrid uncertainties increase the nonlinearities and complexities of the system’s dynamic responses.These findings provide valuable insights into understanding the dynamic behavior of geared system with hybrid uncertainties.
基金supported by the Science and Technology Program of Zhejiang Province of China(2005C11001-02).
文摘Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.
基金supported by National Natural Science Funds for Distinguished Young Scholar under Grant No.70825004National Natural Science Foundation of China under Grant Nos.10731010 and 10628104+3 种基金the National Basic Research Program under Grant No.2007CB814902Creative Research Groups of China under Grant No.10721101supported by leading Academic Discipline Program,211 Project for Shanghai University of Finance and Economics(the 3rd phase)and project number:B803supported by grants from the National Natural Science Foundation of China under Grant No.11071154
文摘This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.
基金Supported by the National Natural Science Foundation of China(11501004,11501005,11526033,11671012)the Natural Science Foundation of Anhui Province(1508085J06,1608085QA02)+1 种基金the Key Projects for Academic Talent of Anhui Province(gxbj ZD2016005)the Research Teaching Model Curriculum of Anhui University(xjyjkc1407)
文摘In this paper, an exponential inequality for the maximal partial sums of negatively superadditive-dependent （NSD, in short） random variables is established. By uSing the exponen- tial inequality, we present some general results on the complete convergence for arrays of rowwise NSD random variables, which improve or generalize the corresponding ones of Wang et al. [28] and Chen et al. [2]. In addition, some sufficient conditions to prove the complete convergence are provided. As an application of the complete convergence that we established, we further investigate the complete consistency and convergence rate of the estimator in a nonparametric regression model based on NSD errors.
基金the National Natural Science Foundation of China （No. 60375003） the Astronautics Basal Science Foundation of China （No. 03153059）.
文摘A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed. The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise. Moreover, the consistence of the test is proved while the rate of convergence is given. The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.
基金China(Grant Nos.11671012,11871072)the Natural Science Foundation of Anhui Province(1808085QA03,1908085QA01,1908085QA07)+1 种基金the Provincial Natural Science Research Project of Anhui Colleges(KJ2019A0001,KJ2019A0003)the Students Innovative Training Project of Anhui University(S201910357342).
文摘We establish some results on the complete moment convergence for weighted sums of widely orthant-dependent(WOD)random variables,which improve and extend the corresponding results of Y.F.Wu,M.G.Zhai,and J.Y.Peng[J.Math.Inequal.,2019,13(1):251–260].As an application of the main results,we investigate the complete consistency for the estimator in a nonparametric regression model based on WOD errors and provide some simulations to verify our theoretical results.
基金The project supported by National Natural Science Foundation of China Crant 18971061
文摘For the linear model y_i=x_iθ+e_i, i=1, 2,…, let the error sequence {e_i}_i=1 be iidr.v.’s, with unknown density f(x). In this paper,a nonparametric estimation method based onthe residuals is proposed for estimating f(x) and the consistency of the estimators is obtained.
基金Supported by the National Natural Science Foundation of China.
文摘Consider the nonparametric regression model Y=go（T）+u,where Y is real-valued, u is a random error,T ranges over a nondegenerate compact interval,say[0,1],and go（·）is an unknown regression function,which is m（m≥0）times continuously differentiable and its ruth derivative,g<sub>0</sub><sup>（m）</sup>,satisfies a H■lder condition of order γ（m +γ】1/2）.A piecewise polynomial L<sub>1</sub>- norm estimator of go is proposed.Under some regularity conditions including that the random errors are independent but not necessarily have a common distribution,it is proved that the rates of convergence of the piecewise polynomial L<sub>1</sub>-norm estimator are o(n<sup>-2（m+γ）+1/m+γ-1/δ</sup>almost surely and o(n<sup>-2（m+γ）+1/m+γ-δ</sup>)in probability,which can arbitrarily approach the optimal rates of convergence for nonparametric regression,where δ is any number in （0, min（（m+γ-1/2）/3,γ））.
基金Supported by the National Natural Science Foundation of China(Grant No.11671338)the Hong Kong Baptist University(Grant Nos.FRG1/16-17/018 and FRG2/16-17/074)
文摘Estimation of the extreme conditional quantiles with functional covariate is an important problem in quantile regression. The existing methods, however, are only applicable for heavy-tailed distributions with a positive conditional tail index. In this paper, we propose a new framework for estimating the extreme conditional quantiles with functional covariate that combines the nonparametric modeling techniques and extreme value theory systematically. Our proposed method is widely applicable, no matter whether the conditional distribution of a response variable Y given a vector of functional covariates X is short, light or heavy-tailed. It thus enriches the existing literature.
基金supported by the National Basic Research Program of China(973)(2012CB316402)The National Natural Science Foundation of China(Grant Nos.61332005,61725205)+3 种基金The Research Project of the North Minzu University(2019XYZJK02,2019xYZJK05,2017KJ24,2017KJ25,2019MS002)Ningxia first-classdisciplinc and scientific research projects(electronic science and technology,NXYLXK2017A07)NingXia Provincial Key Discipline Project-Computer ApplicationThe Provincial Natural Science Foundation ofNingXia(NZ17111,2020AAC03219).
文摘Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.
基金Research funding from the City University of Hong Kong under Strategic Research Grant (Project No. 700233)the China National Natural Science Foundation (Grant No. 7097113)
文摘This paper studies the income inequality and economic development relationship by using unbalanced panel data of OECD and non-OECD countries(regions)for the period 1962-2003.The nonparametric estimation results show that income inequality in OECD countries is almost on the backside of the inverted-U relationship,while non-OECD countries are approximately on the foreside,except that the relationship in both country groups shows an upturn at a high level of development.Development has an indirect effect on inequality through control variables,but the modes are different in the two country groups.The model specification tests show that the relationship is not necessarily captured by the conventional quadratic function.The cubic and fourthdegree polynomials,respectively,fit the OECD and non-OECD country groups best.Our finding is robust regardless of whether the specification uses control variables.Development plays a dominant role in mitigating inequality.
文摘This paper presents a measurement of the technical efficiency of textile industries with 4-digit codes in China by using the cross-section data from 2002 and 2007 and a fully nonparametric stochastic frontier estimation approach. The technical efficiency of these textile industries is compared across six economic ownership types and across seven regions in China. This uncovers the effects of the proprietary characteristics and the location of a firm on its technical efficiency performance. The nonparametric estimation provides some interesting findings. First, textile production in China performs with a decreasing return to scale. The difference between the output elasticity of labor and that of capital decreases from the year 2002 to 2007. Second, the technical efficiency of the 4-digit textile industry in China is significantly contingent on its ownership and location. Privately-run textile enterprises on average perform with the highest level of technical efficiency among the six ownership types while state-owned enterprises perform with the lowest level of technical efficiency, whether or not the location dummies are accounted for. Third, the technical efficiency evaluated by regions follows the order： ＂eastern area 〉 southern area 〉 central area 〉 northern area,＂ which remains unchanged across the two years.
