The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this...The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this paper, a simple test for heteroscedasticity is proposed in nonparametric regression based on residual analysis. Furthermore, some simulations with a comparison with Dette and Munk＇s method are conducted to evaluate the performance of the proposed test. The results demonstrate that the method in this paper performs quite satisfactorily and is much more powerful than Dette and Munk＇s method in some cases.展开更多
The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discrimin...The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.展开更多
A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DE...A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.展开更多
Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures,or stresses.In this paper,we propose a nonparametric spectrum estimation method to get phase dispersion characteri...Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures,or stresses.In this paper,we propose a nonparametric spectrum estimation method to get phase dispersion characteristics and attenuation coefficient.By designing an appropriate vector filter,phase velocity,attenuation coefficient and amplitude can be inverted from the waveform recorded by the receiver array.Performance analysis of this algorithm is compared with Extended Prony Method(EPM)and Forward and Backward Matrix Pencil(FBMP)method.Based on the analysis results,the proposed method is capable of achieving high resolution and precision as the parametric spectrum estimation methods.At the meantime,it also keeps high stability as the other nonparametric spectrum estimation methods.At last,applications to synthetic waveforms modeled using finite difference method and real data show its efficiency.The real data processing results show that the P-wave attenuation log is more sensitive to oil formation compared to S-wave;and the S-wave attenuation log is more sensitive to shale formation compared to P-wave.展开更多
A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underly...A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.展开更多
The number of countries participating in China’s Belt and Road Initiative(BRI)has been increasing since its official launch in 2013.Although the number of BRI participating countries has reached 145,only some are dir...The number of countries participating in China’s Belt and Road Initiative(BRI)has been increasing since its official launch in 2013.Although the number of BRI participating countries has reached 145,only some are directly connected with China through land,sea,and other trade routes developed under the BRI project.Because of their direct links with China,these countries have significantly increased their trade with China.In addition to increased trade,these countries have also been undergoing financial development(FD).Since trade and financial development are closely related to the production of goods and services,therefore;both of these are expected to have environmental impact.The current study examines the effect of trade and FD on carbon dioxide(CO2)emissions in selected BRI countries for the period 2001–2019.This study follows a proper estimation strategy based on preliminary tests,cointegration analysis,and coefficient estimation.The results suggest that trade between China and selected BRI countries has no significant effect on CO2 emissions,whereas,financial development has significantly increased CO2 emissions in these countries.Moreover,BRI countries’imports from China significantly reduce CO2 emissions,whereas their exports to China significantly increase CO2 emission in the BRI countries.The policy recommendations suggest that these BRI countries should leverage their direct connections with China for technology transfer.By utilizing environmentally friendly technology,these countries could also reduce the pollution associated with their exports to China and the rest of the world.Furthermore,their financial sectors should divert funds to industries advancing trade in environmental rather than pollution-intensive goods.展开更多
Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are ...Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are not only undocumented but also often overlooked and not included in National Administrative Registers. Accordingly, find it hard to participate in Social and Economic Affairs. There has been a major push by UNHCR and international partners to “map” the size of stateless populations and their demographic profile, as well as causes, potential solutions and human rights situation. One of the requirements by the UNHCR in their push is for countries to find a potential solution to statelessness which starts with classifying/associating a person from these communities to a particular local community that is recognized in Kenya. This paper addresses this problem by adopting a Robust Nonparametric Kernel Discriminant function to correctly classify the stateless communities in Kenya and compare the performance of this method with the existing techniques through their classification rates. This is because Non-parametric functions have proven to be more robust and useful especially when there exists auxiliary information which can be used to increase precision. The findings from this paper indicate that Nonparametric discriminant classifiers provide a good classification method for classifying the stateless communities in Kenya. This is because they exhibit lower classification rates compared to the parametric methods such as Linear and Quadratic discriminant functions. In addition, the finding shows that based on certain similarities in characteristics that exist in these communities that surround the Pemba Community, the Pemba community can be classified as Giriama or Rabai in which they seem to have a strong link. In this regard, the study recommends the use of the Kernel discriminant classifiers in classifying the stateless persons and that the Government of Kenya consider integrating/recognizing the Pemba community into Giriama or Rabai so that they can be issued with the National Identification Cards and be recognized as Kenyans.展开更多
For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement cos...For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement costs,time intervals for degradation measurements are usually very long,and thus,the value of change-points cannot be determined.Conventionally,a certain degradation measurement is selected as the change-point in a two-phase degradation process.According to the tendency of the two-phase degradation process,the change-point is probably located in the interval between two neighboring degradation measurements,and it is a fuzzy variable.The imprecision of the change-point may lead to the incorrect product’s reliability evaluation results.In this paper,based on the fuzzy theory,a two-phase degradation model with a fuzzy change-point and a statistical analysis method are proposed.First,a two-phase Wiener degradation model is developed according to the membership function of the change-point.Second,the reliability evaluation is carried out using maximum likelihood estimation and a fuzzy simulation approach.Finally,the proposed methodology is verified via a case study.The results of the study show that the proposed methodology can achieve more believable reliability evaluation results compared with those of the conventional approach.展开更多
The nonparametric kernel estimation of probability density function (PDF) pro-vides a uniform and accurate estimate of flood frequency-magnitude relationship.However, the kernel estimate has the disadvantage that the ...The nonparametric kernel estimation of probability density function (PDF) pro-vides a uniform and accurate estimate of flood frequency-magnitude relationship.However, the kernel estimate has the disadvantage that the smoothing factor h is estimate empirically and is not locally adjusted, thus possibly resulting in deteri oration of density estimate when PDF is not smooth and is heavy-tailed. Such a problem can be alleviate by estimating the density of a transformed random vari able, and then taking the inverse transform. A new and efficient circular transform is proposed and investigated in this paper展开更多
观点分析对于社交媒体这一关键的网络舆论阵地有着重要的现实意义。该文基于非参数模型的文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现用户群体所持有的不同立场。针对社交媒体文本长度短、数量多、情感丰富等特点,该...观点分析对于社交媒体这一关键的网络舆论阵地有着重要的现实意义。该文基于非参数模型的文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现用户群体所持有的不同立场。针对社交媒体文本长度短、数量多、情感丰富等特点,该文提出使用情感分布增强(Sentiment Distribution Enhanced,SDE)方法改进现有基于狄利克雷过程混合模型的短文本流聚类算法,以高斯分布建模文本情感,并推导相应的坍缩吉布斯采样算法推断参数。该方法在捕获文本情感特征的同时,能够自动确定聚类簇数量并实现观点聚类。与现有先进方法在Tweets、Google News数据集上的对比实验显示,该文提出的方法在标准化互信息、准确度等指标上取得了超越现有模型的聚类表现,并且在主观性较强的数据集上具有更显著的优势。展开更多
基金the National Natural Science Foundation of China (10531030)
文摘The importance of detecting heteroscedasticity in regression analysis is widely recognized because efficient inference for the regression function requires that heteroscedasticity should be taken into account. In this paper, a simple test for heteroscedasticity is proposed in nonparametric regression based on residual analysis. Furthermore, some simulations with a comparison with Dette and Munk＇s method are conducted to evaluate the performance of the proposed test. The results demonstrate that the method in this paper performs quite satisfactorily and is much more powerful than Dette and Munk＇s method in some cases.
文摘The problem of classification in situations where the assumption of normality in the data is violated, and there are non-linear clustered structures in the dataset is addressed. A robust nonparametric kernel discriminant classification function, which is able to address this challenge, has been developed and the misclassification rates computed for various bandwidth matrices. A comparison with existing parametric classification functions such as the linear discriminant and quadratic discriminant is conducted to evaluate the performance of this classification function using simulated datasets. The results presented in this paper show good performance in terms of misclassification rates for the kernel discriminant classifier when the correct bandwidth is selected as compared to other identified existing classifiers. In this regard, the study recommends the use of the proposed kernel discriminant classification rule when one wishes to classify units into one of several categories or population groups where parametric classifiers might not be applicable.
基金Project(40901216)supported by the National Natural Science Foundation of China
文摘A novel supervised dimensionality reduction algorithm, named discriminant embedding by sparse representation and nonparametric discriminant analysis(DESN), was proposed for face recognition. Within the framework of DESN, the sparse local scatter and multi-class nonparametric between-class scatter were exploited for within-class compactness and between-class separability description, respectively. These descriptions, inspired by sparse representation theory and nonparametric technique, are more discriminative in dealing with complex-distributed data. Furthermore, DESN seeks for the optimal projection matrix by simultaneously maximizing the nonparametric between-class scatter and minimizing the sparse local scatter. The use of Fisher discriminant analysis further boosts the discriminating power of DESN. The proposed DESN was applied to data visualization and face recognition tasks, and was tested extensively on the Wine, ORL, Yale and Extended Yale B databases. Experimental results show that DESN is helpful to visualize the structure of high-dimensional data sets, and the average face recognition rate of DESN is about 9.4%, higher than that of other algorithms.
基金This research was supported by the National Natural Science Foundation of China(No.42274141)Science Foundation of China University of Petroleum,Beijing(No.2462020YXZZ007).
文摘Dispersion and attenuation analysis can be used to determine formation anisotropy induced by fractures,or stresses.In this paper,we propose a nonparametric spectrum estimation method to get phase dispersion characteristics and attenuation coefficient.By designing an appropriate vector filter,phase velocity,attenuation coefficient and amplitude can be inverted from the waveform recorded by the receiver array.Performance analysis of this algorithm is compared with Extended Prony Method(EPM)and Forward and Backward Matrix Pencil(FBMP)method.Based on the analysis results,the proposed method is capable of achieving high resolution and precision as the parametric spectrum estimation methods.At the meantime,it also keeps high stability as the other nonparametric spectrum estimation methods.At last,applications to synthetic waveforms modeled using finite difference method and real data show its efficiency.The real data processing results show that the P-wave attenuation log is more sensitive to oil formation compared to S-wave;and the S-wave attenuation log is more sensitive to shale formation compared to P-wave.
文摘A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.
文摘The number of countries participating in China’s Belt and Road Initiative(BRI)has been increasing since its official launch in 2013.Although the number of BRI participating countries has reached 145,only some are directly connected with China through land,sea,and other trade routes developed under the BRI project.Because of their direct links with China,these countries have significantly increased their trade with China.In addition to increased trade,these countries have also been undergoing financial development(FD).Since trade and financial development are closely related to the production of goods and services,therefore;both of these are expected to have environmental impact.The current study examines the effect of trade and FD on carbon dioxide(CO2)emissions in selected BRI countries for the period 2001–2019.This study follows a proper estimation strategy based on preliminary tests,cointegration analysis,and coefficient estimation.The results suggest that trade between China and selected BRI countries has no significant effect on CO2 emissions,whereas,financial development has significantly increased CO2 emissions in these countries.Moreover,BRI countries’imports from China significantly reduce CO2 emissions,whereas their exports to China significantly increase CO2 emission in the BRI countries.The policy recommendations suggest that these BRI countries should leverage their direct connections with China for technology transfer.By utilizing environmentally friendly technology,these countries could also reduce the pollution associated with their exports to China and the rest of the world.Furthermore,their financial sectors should divert funds to industries advancing trade in environmental rather than pollution-intensive goods.
文摘Statelessness is the absence of any Nationality. These include the Pemba, Shona, Galjeel, people of Burundi and Rwanda descent, and children born in Kenya to British Overseas Citizens after 1983. Frequently, they are not only undocumented but also often overlooked and not included in National Administrative Registers. Accordingly, find it hard to participate in Social and Economic Affairs. There has been a major push by UNHCR and international partners to “map” the size of stateless populations and their demographic profile, as well as causes, potential solutions and human rights situation. One of the requirements by the UNHCR in their push is for countries to find a potential solution to statelessness which starts with classifying/associating a person from these communities to a particular local community that is recognized in Kenya. This paper addresses this problem by adopting a Robust Nonparametric Kernel Discriminant function to correctly classify the stateless communities in Kenya and compare the performance of this method with the existing techniques through their classification rates. This is because Non-parametric functions have proven to be more robust and useful especially when there exists auxiliary information which can be used to increase precision. The findings from this paper indicate that Nonparametric discriminant classifiers provide a good classification method for classifying the stateless communities in Kenya. This is because they exhibit lower classification rates compared to the parametric methods such as Linear and Quadratic discriminant functions. In addition, the finding shows that based on certain similarities in characteristics that exist in these communities that surround the Pemba Community, the Pemba community can be classified as Giriama or Rabai in which they seem to have a strong link. In this regard, the study recommends the use of the Kernel discriminant classifiers in classifying the stateless persons and that the Government of Kenya consider integrating/recognizing the Pemba community into Giriama or Rabai so that they can be issued with the National Identification Cards and be recognized as Kenyans.
基金the National Natural Science Foundation of China(No.61703391)。
文摘For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement costs,time intervals for degradation measurements are usually very long,and thus,the value of change-points cannot be determined.Conventionally,a certain degradation measurement is selected as the change-point in a two-phase degradation process.According to the tendency of the two-phase degradation process,the change-point is probably located in the interval between two neighboring degradation measurements,and it is a fuzzy variable.The imprecision of the change-point may lead to the incorrect product’s reliability evaluation results.In this paper,based on the fuzzy theory,a two-phase degradation model with a fuzzy change-point and a statistical analysis method are proposed.First,a two-phase Wiener degradation model is developed according to the membership function of the change-point.Second,the reliability evaluation is carried out using maximum likelihood estimation and a fuzzy simulation approach.Finally,the proposed methodology is verified via a case study.The results of the study show that the proposed methodology can achieve more believable reliability evaluation results compared with those of the conventional approach.
文摘The nonparametric kernel estimation of probability density function (PDF) pro-vides a uniform and accurate estimate of flood frequency-magnitude relationship.However, the kernel estimate has the disadvantage that the smoothing factor h is estimate empirically and is not locally adjusted, thus possibly resulting in deteri oration of density estimate when PDF is not smooth and is heavy-tailed. Such a problem can be alleviate by estimating the density of a transformed random vari able, and then taking the inverse transform. A new and efficient circular transform is proposed and investigated in this paper
文摘观点分析对于社交媒体这一关键的网络舆论阵地有着重要的现实意义。该文基于非参数模型的文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现用户群体所持有的不同立场。针对社交媒体文本长度短、数量多、情感丰富等特点,该文提出使用情感分布增强(Sentiment Distribution Enhanced,SDE)方法改进现有基于狄利克雷过程混合模型的短文本流聚类算法,以高斯分布建模文本情感,并推导相应的坍缩吉布斯采样算法推断参数。该方法在捕获文本情感特征的同时,能够自动确定聚类簇数量并实现观点聚类。与现有先进方法在Tweets、Google News数据集上的对比实验显示,该文提出的方法在标准化互信息、准确度等指标上取得了超越现有模型的聚类表现,并且在主观性较强的数据集上具有更显著的优势。
