In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations i...In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China(31 Chinese provinces are considered except Hong Kong, Macao, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan'.展开更多
A simple semi-empirical analysis method for predicting the group effect of pile group under dragload embedded in clay was described assuming an effective influence area around various locations of pile group. Various ...A simple semi-empirical analysis method for predicting the group effect of pile group under dragload embedded in clay was described assuming an effective influence area around various locations of pile group. Various pile and soil parameters such as the array of pile group, spacing of the piles (S), embedment length to diameter ratio of piles (L/D) and the soil properties such as density (γ), angle of internal friction (φ) and pile-soil interface friction coefficient (μ) were considered in the analysis. Model test for dragload of pile group on viscosity soil layer under surface load consolidation conditions was studied. The variations of dragload of pile, resistance of pile tip and the layered settlement of soil with consolidation time were measured. In order to perform comparative analysis, single pile was tested in the same conditions. The predicted group effect values of pile group under dragload were then compared with model test results carried out as a part of the present investigation and also with the values reported in literatures. The predicted values were found to be in good agreement with the measured values, validating the developed analysis method. The model test results show that negative skin friction of pile shaft will reach 80%-90% of its maximum value, when pile-soil relative displacement reaches 2 mm.展开更多
Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions b...Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.展开更多
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of pa...We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.展开更多
The HIV infection model of CD4＋ T-cells corresponds to a class of nonlinear ordinary differential equation systems. In this study, we provide the approximate solution of this model by using orthonormal Bernstein poly...The HIV infection model of CD4＋ T-cells corresponds to a class of nonlinear ordinary differential equation systems. In this study, we provide the approximate solution of this model by using orthonormal Bernstein polynomials （OBPs）. By applying the proposed method, the nonlinear system of ordinary differential equations reduces to a nonlinear system of algebraic equations which can be solved by using a suitable numerical method such as Newton＇s method. We prove some useful theorems concerning the convergence and error estimate associated to the present method. Finally, we apply the proposed method to get the numerical solution of this model with the arbitrary initial conditions and values. Furthermore, the numerical results obtained by the suggested method are compared with the results achieved by other previous methods. These results indicate that this method agrees with other previous methods.展开更多
The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only ...The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only recently and the asymptotic theory of parameter estimation for the DCC model has not yet to be fully discussed. In this paper, we propose an alternative model, namely the scalar dynamic conditional correlation(SDCC) model. Sufficient and easily-checked conditions for stationarity, geometric ergodicity, andβ-mixing with exponential-decay rates are provided. We then show the strong consistency and asymptotic normality of the quasi-maximum-likelihood estimator(QMLE) of the model parameters under regular conditions.The asymptotic results are illustrated by Monte Carlo experiments. As a real-data example, the proposed SDCC model is applied to analyzing the daily returns of the FSTE(financial times and stock exchange) 100 index and FSTE 100 futures. Our model improves the performance of the DCC model in the sense that the Li-Mc Leod statistic of the SDCC model is much smaller and the hedging efficiency is higher.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.71371160)the Program for Changjiang Youth Scholars(No.Q2016131)the Program for New Century Excellent Talents in University(No.NCET-13-0509)
文摘In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China(31 Chinese provinces are considered except Hong Kong, Macao, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan'.
基金Project(50679015) supported by the National Natural Science Foundation of China
文摘A simple semi-empirical analysis method for predicting the group effect of pile group under dragload embedded in clay was described assuming an effective influence area around various locations of pile group. Various pile and soil parameters such as the array of pile group, spacing of the piles (S), embedment length to diameter ratio of piles (L/D) and the soil properties such as density (γ), angle of internal friction (φ) and pile-soil interface friction coefficient (μ) were considered in the analysis. Model test for dragload of pile group on viscosity soil layer under surface load consolidation conditions was studied. The variations of dragload of pile, resistance of pile tip and the layered settlement of soil with consolidation time were measured. In order to perform comparative analysis, single pile was tested in the same conditions. The predicted group effect values of pile group under dragload were then compared with model test results carried out as a part of the present investigation and also with the values reported in literatures. The predicted values were found to be in good agreement with the measured values, validating the developed analysis method. The model test results show that negative skin friction of pile shaft will reach 80%-90% of its maximum value, when pile-soil relative displacement reaches 2 mm.
基金supported by the Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201506002, CRA40: 40-year CMA global atmospheric reanalysis)the National Basic Research Program of China (Grant No. 2015CB953703)+1 种基金the Intergovernmental Key International S & T Innovation Cooperation Program (Grant No. 2016YFE0102400)the National Natural Science Foundation of China (Grant Nos. 41305052 & 41375139)
文摘Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.
基金supported by the National Natural Science Foundation of China(Grant No.11404113)the Guangzhou Key Laboratory of Brain Computer Interaction and Applications(Grant No.201509010006)
文摘We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved,it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives:(1) maximizing the Fisher information, improving the parameter estimation precision, and(2)minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ε-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
文摘The HIV infection model of CD4＋ T-cells corresponds to a class of nonlinear ordinary differential equation systems. In this study, we provide the approximate solution of this model by using orthonormal Bernstein polynomials （OBPs）. By applying the proposed method, the nonlinear system of ordinary differential equations reduces to a nonlinear system of algebraic equations which can be solved by using a suitable numerical method such as Newton＇s method. We prove some useful theorems concerning the convergence and error estimate associated to the present method. Finally, we apply the proposed method to get the numerical solution of this model with the arbitrary initial conditions and values. Furthermore, the numerical results obtained by the suggested method are compared with the results achieved by other previous methods. These results indicate that this method agrees with other previous methods.
基金supported by National Natural Science Foundation of China(Grant No.71771224)National Social Science Foundation of China(Grant Nos.14ZDA044 and 15BGJ037)+1 种基金the Program for National Statistics Science Research Plan(Grant No.2016LD02)the Program for Innovation Research in Central University of Finance and Economics
文摘The dynamic conditional correlation(DCC) model has been widely used for modeling the conditional correlation of multivariate time series by Engle(2002). However, the stationarity conditions have been established only recently and the asymptotic theory of parameter estimation for the DCC model has not yet to be fully discussed. In this paper, we propose an alternative model, namely the scalar dynamic conditional correlation(SDCC) model. Sufficient and easily-checked conditions for stationarity, geometric ergodicity, andβ-mixing with exponential-decay rates are provided. We then show the strong consistency and asymptotic normality of the quasi-maximum-likelihood estimator(QMLE) of the model parameters under regular conditions.The asymptotic results are illustrated by Monte Carlo experiments. As a real-data example, the proposed SDCC model is applied to analyzing the daily returns of the FSTE(financial times and stock exchange) 100 index and FSTE 100 futures. Our model improves the performance of the DCC model in the sense that the Li-Mc Leod statistic of the SDCC model is much smaller and the hedging efficiency is higher.
