How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influenti...How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influential factors, the energy consumption, the proportion of tertiary industry in gross domestic product （GDP）, and the degree of dependence on foreign trade, are carefully selected, since all of them have closer grey relation with China＇s COz emissions compared with others when the grey relational analysis （GRA） method is applied. The study highlights co-integration relation of these four variables using the co-integration analysis method. And then a long-term co-integration equation and a short-term error correction model of China＇s CO2 emissions are devel- oped. Finally, the comparison is exerted between the forecast value and the actual value of China＇s CO2 emissions based on error correction model. The results and the relevant statistics tests show that the pro- posed model has better explanation capability and credibility.展开更多
Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its tes...Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its testing principle of the wear depth of the spherical plain bearing was introduced.Meanwhile,the error factors affecting the wear-depth detecting precision were analyzed.Then,the comprehensive error model of the wear-depth detecting system of the spherical plain bearing was built by the multi-body system theory(MBS).In addition,the thermal deformation of the wear-depth detecting system caused by varying the environmental temperature was detected.Finally,according to the above experimental parameters,the thermal errors of the related parts of the comprehensive error model were calculated by FEM.The results show that the difference between the simulation value and the experimental value is less than 0.005 mm,and the two values are close.The correctness of the comprehensive error model is verified under the thermal error experimental conditions.展开更多
Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal c...Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.展开更多
Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the mo...Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the moderate deviation estimates to hypothesis testing for signal detection problem we give a decision region such that its error probability of the second kind tends to zero with faster speed than the error probability of the first kind when the error probability of the first kind is approximated by e-ατ（T）, where α〉 0, τ（T） = o（T） and τ（T）→∞ as the observation time T goes to infinity.展开更多
This paper analyzes the problem of testing for parameters change in ARCH errors models with deterministic trend based on residual cusum test. It is shown that the asymptotically limiting distribution of the residual c...This paper analyzes the problem of testing for parameters change in ARCH errors models with deterministic trend based on residual cusum test. It is shown that the asymptotically limiting distribution of the residual cusum test statistic is still the sup of a standard Brownian bridge under null hypothesis. In order to check this, we carry out a Monte Carlo simulation and examine the return of IBM data. The results from both simulation and real data analysis support our claim. We also can explain this phenomenon from a theoretical viewpoint that the variance in ARCH model in mainly determined by its parameters.展开更多
基金Supported by the National Natural Science Foundation of China(41101569)the China Postdoctoral Science Foundation Funded Project(2011M500965)+5 种基金the Jiangsu Funds of Social Science(11EYC023)the Doctoral Discipline New Teachers Fund(20110095120002)the Jiangsu Postdoctoral Science Foundation Funded Project(1102088C)the Fundamental Research Funds for the Central Universities(JGJ110763)the Talent Introduction Funds of China University of Mining and Technologythe Sail Plan Funds for Young Teachers of China University of Mining and Technology~~
文摘How to achieve the objective of reducing CO2 emissions has been an academic focus in China recently. The factors influencing CO2 emissions are the vital issue to accomplish the arduous target. Firstly, three influential factors, the energy consumption, the proportion of tertiary industry in gross domestic product （GDP）, and the degree of dependence on foreign trade, are carefully selected, since all of them have closer grey relation with China＇s COz emissions compared with others when the grey relational analysis （GRA） method is applied. The study highlights co-integration relation of these four variables using the co-integration analysis method. And then a long-term co-integration equation and a short-term error correction model of China＇s CO2 emissions are devel- oped. Finally, the comparison is exerted between the forecast value and the actual value of China＇s CO2 emissions based on error correction model. The results and the relevant statistics tests show that the pro- posed model has better explanation capability and credibility.
基金Project(2014E00468R)supported by Technological Innovation Fund of Aviation Industry Corporation of China
文摘Because of various error factors,the detecting errors in the real-time experimental data of the wear depth affect the accuracy of the detecting data.The self-made spherical plain bearing tester was studied,and its testing principle of the wear depth of the spherical plain bearing was introduced.Meanwhile,the error factors affecting the wear-depth detecting precision were analyzed.Then,the comprehensive error model of the wear-depth detecting system of the spherical plain bearing was built by the multi-body system theory(MBS).In addition,the thermal deformation of the wear-depth detecting system caused by varying the environmental temperature was detected.Finally,according to the above experimental parameters,the thermal errors of the related parts of the comprehensive error model were calculated by FEM.The results show that the difference between the simulation value and the experimental value is less than 0.005 mm,and the two values are close.The correctness of the comprehensive error model is verified under the thermal error experimental conditions.
基金The National Natural Science Foundation of China(No 60504033)
文摘Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.
基金supported by National Natural Science Foundation of China (Grant Nos.10871153 and 11171262)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200804860048)
文摘Abstract We use moderate deviations to study the signal detection problem for a diffusion model. We establish a moderate deviation principle for the log-likelihood function of the diffusion model. Then applying the moderate deviation estimates to hypothesis testing for signal detection problem we give a decision region such that its error probability of the second kind tends to zero with faster speed than the error probability of the first kind when the error probability of the first kind is approximated by e-ατ（T）, where α〉 0, τ（T） = o（T） and τ（T）→∞ as the observation time T goes to infinity.
基金the National Natural Science Foundation of China (Nos.60375003 60972150)the Science and Technology Innovation Foundation of Northwestern Polytechnical University (No.2007KJ01033)
文摘This paper analyzes the problem of testing for parameters change in ARCH errors models with deterministic trend based on residual cusum test. It is shown that the asymptotically limiting distribution of the residual cusum test statistic is still the sup of a standard Brownian bridge under null hypothesis. In order to check this, we carry out a Monte Carlo simulation and examine the return of IBM data. The results from both simulation and real data analysis support our claim. We also can explain this phenomenon from a theoretical viewpoint that the variance in ARCH model in mainly determined by its parameters.
