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
Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each l...Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.展开更多
This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenari...This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.展开更多
We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using wee...We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using weekly data from November 2003 to December 2020.We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators.We find,in general,strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors,with stronger impacts for negative bubbles.Our findings have critical implications for the Reserve Bank of India,academics,and investors.展开更多
The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influ...The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influence of policy constraints when evaluating MAC.To address this gap,this paper proposes a nonparametric directional distance function approach under policy regulations to estimate the CO_(2)shadow price in the construction industry across 30 Chinese provinces from 2010 to 2017.Based on this enhanced method,four key findings emerge:①the annual CO_(2)shadow price exhibits an overall upward trend during the observation period;②regional shadow prices display marked variation across provinces;③regional heterogeneity in shadow prices has increased steadily over time;and④both urbanization rate and energy consumption per unit of gross domestic product are significantly negatively correlated with the CO_(2)shadow price.Finally,the paper offers several policy recommendations for CO_(2)emissions reduction in the Chinese construction industry at the national,industry,and enterprise levels.展开更多
In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold val...In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.展开更多
A new algorithm based on the projection method with the implicit finite difference technique was established to calculate the velocity fields and pressure.The calculation region can be divided into different regions a...A new algorithm based on the projection method with the implicit finite difference technique was established to calculate the velocity fields and pressure.The calculation region can be divided into different regions according to Reynolds number.In the far-wall region,the thermal melt flow was calculated as Newtonian flow.In the near-wall region,the thermal melt flow was calculated as non-Newtonian flow.It was proved that the new algorithm based on the projection method with the implicit technique was correct through nonparametric statistics method and experiment.The simulation results show that the new algorithm based on the projection method with the implicit technique calculates more quickly than the solution algorithm-volume of fluid method using the explicit difference method.展开更多
A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown m...A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.展开更多
In time series modeling, the residuals are often checked for white noise and normality. In practice, the useful tests are Ljung Box test. Mcleod Li test and Lin Mudholkar test. In this paper, we present a nonparame...In time series modeling, the residuals are often checked for white noise and normality. In practice, the useful tests are Ljung Box test. Mcleod Li test and Lin Mudholkar test. In this paper, we present a nonparametric approach for checking the residuals of time series models. This approach is based on the maximal correlation coefficient ρ 2 * between the residuals and time t . The basic idea is to use the bootstrap to form the null distribution of the statistic ρ 2 * under the null hypothesis H 0:ρ 2 * =0. For calculating ρ 2 * , we proposes a ρ algorithm, analogous to ACE procedure. Power study shows this approach is more powerful than Ljung Box test. Meanwhile, some numerical results and two examples are reported in this paper.展开更多
Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the cor...Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the corresponding classifier.展开更多
Based on the runoff and meteorological data of Langan（兰干） Hydrological Station from 1957 to 2009 in Keriya（克里雅） River,the periodicities,abrupt changes,and trends of climate factors and runoff were investigate...Based on the runoff and meteorological data of Langan（兰干） Hydrological Station from 1957 to 2009 in Keriya（克里雅） River,the periodicities,abrupt changes,and trends of climate factors and runoff were investigated by wavelet analysis and nonparametric test;then,the future change of the annual runoff was predicted by a periodic trend superposition model.In succession,the influencing volumes of climate change on the annual runoff were separated from the observation values of the an-nual runoff in Keriya River.The results show that（1） temperature series increased significantly,while the annual runoff and precipitation of Keriya River increased insignificantly at the significant level of α=0.05;（2） the common periods of 9 and 15 years existed in the annual runoff evolution process,and the primary periods of temperature and precipitation were 9 and 22 years and 9 and 13 years,respec-tively;（3） the annual runoff did not vary simultaneously with the abrupt change of climate factors in the headstream;the abrupt points of annual runoff and temperature are at 1998 and 1980 year,and that of precipitation is not so significant;and（4） the annual runoff will experience a decrease trend in the future period;the total increasing volume owing to climate change is 23.154×108 m3 in the head-stream during the period of 1999-2009;however,the stream flow has been nearly utilized completely due to the human activities in the mainstream area of Keriya River.展开更多
As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configu...As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.展开更多
Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have no...Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply.展开更多
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; ...In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.展开更多
To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accura...To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.展开更多
This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The ana...This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The analysis is carried out for both fluctuating and nonfluctuating received signals. The simulation results show that the new proposed detector has the best detection performance in homogeneous as well as nonhomogeneous background conditions, while TGS procedure is better than the GS detector in distinguishing the primary target from the secondary interfering ones.展开更多
It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when th...It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.展开更多
This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine ...This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.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
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.
基金supported in part by the Gansu Haizhi Characteristic Demonstration Project(No.GSHZTS2022-2).
文摘Since the introduction of vision Transformers into the computer vision field,many vision tasks such as semantic segmentation tasks,have undergone radical changes.Although Transformer enhances the correlation of each local feature of an image object in the hidden space through the attention mechanism,it is difficult for a segmentation head to accomplish the mask prediction for dense embedding of multi-category and multi-local features.We present patch prototype vision Transformer(PPFormer),a Transformer architecture for semantic segmentation based on knowledge-embedded patch prototypes.1)The hierarchical Transformer encoder can generate multi-scale and multi-layered patch features including seamless patch projection to obtain information of multiscale patches,and feature-clustered self-attention to enhance the interplay of multi-layered visual information with implicit position encodes.2)PPFormer utilizes a non-parametric prototype decoder to extract region observations which represent significant parts of the objects by unlearnable patch prototypes and then calculate similarity between patch prototypes and pixel embeddings.The proposed contrasting patch prototype alignment module,which uses new patch prototypes to update prototype bank,effectively maintains class boundaries for prototypes.For different application scenarios,we have launched PPFormer-S,PPFormer-M and PPFormer-L by expanding the scale.Experimental results demonstrate that PPFormer can outperform fully convolutional networks(FCN)-and attention-based semantic segmentation models on the PASCAL VOC 2012,ADE20k,and Cityscapes datasets.
文摘This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.
文摘We employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator(MS-LPPLS-CI)approach to identify positive and negative bubbles in the short-,medium,and long-term for the Indian stock market,using weekly data from November 2003 to December 2020.We use a nonparametric causality-in-quantiles approach to analyze the predictive impact of monetary policy shocks on bubble indicators.We find,in general,strong evidence of predictability across the entire conditional distribution for the two monetary policy shock factors,with stronger impacts for negative bubbles.Our findings have critical implications for the Reserve Bank of India,academics,and investors.
基金support of Dean Fund Project of the China National Institute of Standardization[Grant No.572025Y-12477]National Natural Science Foundation of China[Grant No.72204247].
文摘The marginal abatement cost(MAC)of CO_(2)emissions is one of the most critical indicators used to assess energy conservation and emission reduction.Although extensively measured,few studies have incorporated the influence of policy constraints when evaluating MAC.To address this gap,this paper proposes a nonparametric directional distance function approach under policy regulations to estimate the CO_(2)shadow price in the construction industry across 30 Chinese provinces from 2010 to 2017.Based on this enhanced method,four key findings emerge:①the annual CO_(2)shadow price exhibits an overall upward trend during the observation period;②regional shadow prices display marked variation across provinces;③regional heterogeneity in shadow prices has increased steadily over time;and④both urbanization rate and energy consumption per unit of gross domestic product are significantly negatively correlated with the CO_(2)shadow price.Finally,the paper offers several policy recommendations for CO_(2)emissions reduction in the Chinese construction industry at the national,industry,and enterprise levels.
文摘In the multilevel thresholding segmentation of the image, the classification number is always given by the supervisor. To solve this problem, a fast multilevel thresholding algorithm considering both the threshold value and the classification number is proposed based on the maximum entropy, and the self-adaptive criterion of the classification number is given. The algorithm can obtain thresholds and automatically decide the classification number. Experimental results show that the algorithm is effective.
基金Project (50975263) supported by the National Natural Science Foundation of ChinaProject (2010081015) supported by International Cooperation Project of Shanxi Province, China+1 种基金 Project (2010-78) supported by the Scholarship Council in Shanxi province, ChinaProject (2010420120005) supported by Doctoral Fund of Ministry of Education of China
文摘A new algorithm based on the projection method with the implicit finite difference technique was established to calculate the velocity fields and pressure.The calculation region can be divided into different regions according to Reynolds number.In the far-wall region,the thermal melt flow was calculated as Newtonian flow.In the near-wall region,the thermal melt flow was calculated as non-Newtonian flow.It was proved that the new algorithm based on the projection method with the implicit technique was correct through nonparametric statistics method and experiment.The simulation results show that the new algorithm based on the projection method with the implicit technique calculates more quickly than the solution algorithm-volume of fluid method using the explicit difference method.
基金Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China(No.3104001014)
文摘A nonparametric Bayesian method is presented to classify the MPSK （M-ary phase shift keying） signals. The MPSK signals with unknown signal noise ratios （SNRs） are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals. The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart （NIW） distribution as the prior probability of the unknown mean and covariance. Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain （MCMC） random sampling algorithm. By iterations, the density estimation of the MPSK signals can be estimated. Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR 〉5 dB and 1 600 symbols are used in this method.
文摘In time series modeling, the residuals are often checked for white noise and normality. In practice, the useful tests are Ljung Box test. Mcleod Li test and Lin Mudholkar test. In this paper, we present a nonparametric approach for checking the residuals of time series models. This approach is based on the maximal correlation coefficient ρ 2 * between the residuals and time t . The basic idea is to use the bootstrap to form the null distribution of the statistic ρ 2 * under the null hypothesis H 0:ρ 2 * =0. For calculating ρ 2 * , we proposes a ρ algorithm, analogous to ACE procedure. Power study shows this approach is more powerful than Ljung Box test. Meanwhile, some numerical results and two examples are reported in this paper.
文摘Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the corresponding classifier.
基金supported by the National Basic Research Program of China (No. 2009CB421308)the Ministry of Water Resources Special Fund for Scientific Research on Public Causes (No. 201101049)
文摘Based on the runoff and meteorological data of Langan（兰干） Hydrological Station from 1957 to 2009 in Keriya（克里雅） River,the periodicities,abrupt changes,and trends of climate factors and runoff were investigated by wavelet analysis and nonparametric test;then,the future change of the annual runoff was predicted by a periodic trend superposition model.In succession,the influencing volumes of climate change on the annual runoff were separated from the observation values of the an-nual runoff in Keriya River.The results show that（1） temperature series increased significantly,while the annual runoff and precipitation of Keriya River increased insignificantly at the significant level of α=0.05;（2） the common periods of 9 and 15 years existed in the annual runoff evolution process,and the primary periods of temperature and precipitation were 9 and 22 years and 9 and 13 years,respec-tively;（3） the annual runoff did not vary simultaneously with the abrupt change of climate factors in the headstream;the abrupt points of annual runoff and temperature are at 1998 and 1980 year,and that of precipitation is not so significant;and（4） the annual runoff will experience a decrease trend in the future period;the total increasing volume owing to climate change is 23.154×108 m3 in the head-stream during the period of 1999-2009;however,the stream flow has been nearly utilized completely due to the human activities in the mainstream area of Keriya River.
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.
基金supported by National Natural Science Foundation of China (Grant No. 70931004,Grant No. 70802043)
文摘Profile monitoring is used to check the stability of the quality of a product over time when the product quality is best represented by a function at each time point.However,most previous monitoring approaches have not considered that the argument values may vary from profile to profile,which is common in practice.A novel nonparametric control scheme based on profile error is proposed for monitoring nonlinear profiles with varied argument values.The proposed scheme uses the metrics of profile error as the statistics to construct the control charts.More details about the design of this nonparametric scheme are also discussed.The monitoring performance of the combined control scheme is compared with that of alternative nonparametric methods via simulation.Simulation studies show that the combined scheme is effective in detecting parameter error and is sensitive to small shifts in the process.In addition,due to the properties of the charting statistics,the out-of-control signal can provide diagnostic information for the users.Finally,the implementation steps of the proposed monitoring scheme are given and applied for monitoring the blade manufacturing process.With the application in blade manufacturing of aircraft engines,the proposed nonparametric control scheme is effective,interpretable,and easy to apply.
基金Supported by the Science Development Foundation of HFUT(041002F)
文摘In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
基金The National Natural Science Foundation of China under contract No.61471024the National Marine Technology Program for Public Welfare under contract No.201505002-1the Beijing Higher Education Young Elite Teacher Project under contract No.YETP0514
文摘To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.
文摘This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The analysis is carried out for both fluctuating and nonfluctuating received signals. The simulation results show that the new proposed detector has the best detection performance in homogeneous as well as nonhomogeneous background conditions, while TGS procedure is better than the GS detector in distinguishing the primary target from the secondary interfering ones.
文摘It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.
文摘This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building.
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.
