In view of the deviation of the fitting results of the classical exponential model and the hyperbolic model （the BB model） from several experiment data during intermediate stress period, a new constitutive model for...In view of the deviation of the fitting results of the classical exponential model and the hyperbolic model （the BB model） from several experiment data during intermediate stress period, a new constitutive model for the nonlinear normal deformation of rock joints under normal monotonous load is established with flexibility-deformation method. First of all, basic laws of the deformation of joints under normal monotonous load are discussed, based on which three basic conditions which the complete constitutive equation for rock joints under normal load should meet are put forward. The analysis of the modified normal con- stitutive model on stress-deformation curve shows that the general exponential model and the improved hyperbolic model are not complete in math theory. Flexibility-deformation monotone decreasing curve lying between flexibility-deformation curve of the classical exponential model and the BB model is chosen, which meets basic conditions of normal deformation mentioned before, then a new normal deformation constitutive model of rock joints containing three parameters is established. Two main forms of flexibility-deformation curve are analyzed and specific math formulas of the two forms are deduced. Then the range of the parameters in the g-δ model and the g-2 model and the correlative influence factor in geology are preliminarily discussed. Referring to different experiment data, the validating analysis of the g-δ model and the g-γ model shows that the g-2 model can be applied to both the mated joints and unmated joints. Besides, experiment data can be better fit with the g-2 model with respect to the BB model, the classical exponential model and the logarithm model.展开更多
Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or mor...Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or more explanatory variables and a response variable by fitting a linear equation to the interval-valued observations. Despite of the well-known methods such as CM, CRM and CCRM proposed in the literature, further study is still needed to build a regression model that can capture the complete information in interval-valued observations. To this end, in this paper, we propose the novel Complete Information Method （CIM） for linear regression modeling. By dividing hypercubes into informative grid data, CIM defines the inner product of interval-valued variables, and transforms the regression modeling into the computation of some inner products. Experiments on both the synthetic and real-world data sets demonstrate the merits of CIM in modeling interval-valued data, and avoiding the mathematical incoherence introduced by CM and CRM.展开更多
Herd behavior in financial markets often leads to unjustified macroscopic phenomena.However,despite existing studies on modeling herd behavior,how it varies across individual agents and over time remains unclear.We sh...Herd behavior in financial markets often leads to unjustified macroscopic phenomena.However,despite existing studies on modeling herd behavior,how it varies across individual agents and over time remains unclear.We show that herd behavior in mutual fund companies can be understood from the functional networks representing interactions inferred from investment similarities.Specifically,in this paper,the spatial characteristics of herd behavior stand for the topology relationships of observations in networks.We analyze the collective dynamics of mutual fund investment from 2003 to 2019 in China using the language of network science and show that herding behavior accompanies this industry's development but dwindles after the 2015 Chinese market crash.By integrating community detection analysis,we found an increased degree of coherence in the collective herding behavior of the system,even though the localization of herding behavior decreases for clusters of mutual fund companies when the systemic risk builds up.Further analysis showed that herding behavior impacts the payoff of individual fund companies differently across years.The spatial-temporal changes of herding behavior between mutual funds presented in this paper shed light on the debate of individual versus systemic risk and,thus,could interest regulators and investors.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 50879063 and 50979081) the National Basic Research Program of China ("973" Program) (Grant No. 2011CB013501)
文摘In view of the deviation of the fitting results of the classical exponential model and the hyperbolic model （the BB model） from several experiment data during intermediate stress period, a new constitutive model for the nonlinear normal deformation of rock joints under normal monotonous load is established with flexibility-deformation method. First of all, basic laws of the deformation of joints under normal monotonous load are discussed, based on which three basic conditions which the complete constitutive equation for rock joints under normal load should meet are put forward. The analysis of the modified normal con- stitutive model on stress-deformation curve shows that the general exponential model and the improved hyperbolic model are not complete in math theory. Flexibility-deformation monotone decreasing curve lying between flexibility-deformation curve of the classical exponential model and the BB model is chosen, which meets basic conditions of normal deformation mentioned before, then a new normal deformation constitutive model of rock joints containing three parameters is established. Two main forms of flexibility-deformation curve are analyzed and specific math formulas of the two forms are deduced. Then the range of the parameters in the g-δ model and the g-2 model and the correlative influence factor in geology are preliminarily discussed. Referring to different experiment data, the validating analysis of the g-δ model and the g-γ model shows that the g-2 model can be applied to both the mated joints and unmated joints. Besides, experiment data can be better fit with the g-2 model with respect to the BB model, the classical exponential model and the logarithm model.
基金supported in part by the National Natural Science Foundation of China(NSFC) under Grants 71031001,70771004,70901002 and 71171007the Foundation for the Author of National Excellent Doctoral Dissertation of PR China under Grant 201189the Program for New Century Excellent Talents in University under Grant NCET-1 1-0778
文摘Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or more explanatory variables and a response variable by fitting a linear equation to the interval-valued observations. Despite of the well-known methods such as CM, CRM and CCRM proposed in the literature, further study is still needed to build a regression model that can capture the complete information in interval-valued observations. To this end, in this paper, we propose the novel Complete Information Method （CIM） for linear regression modeling. By dividing hypercubes into informative grid data, CIM defines the inner product of interval-valued variables, and transforms the regression modeling into the computation of some inner products. Experiments on both the synthetic and real-world data sets demonstrate the merits of CIM in modeling interval-valued data, and avoiding the mathematical incoherence introduced by CM and CRM.
文摘Herd behavior in financial markets often leads to unjustified macroscopic phenomena.However,despite existing studies on modeling herd behavior,how it varies across individual agents and over time remains unclear.We show that herd behavior in mutual fund companies can be understood from the functional networks representing interactions inferred from investment similarities.Specifically,in this paper,the spatial characteristics of herd behavior stand for the topology relationships of observations in networks.We analyze the collective dynamics of mutual fund investment from 2003 to 2019 in China using the language of network science and show that herding behavior accompanies this industry's development but dwindles after the 2015 Chinese market crash.By integrating community detection analysis,we found an increased degree of coherence in the collective herding behavior of the system,even though the localization of herding behavior decreases for clusters of mutual fund companies when the systemic risk builds up.Further analysis showed that herding behavior impacts the payoff of individual fund companies differently across years.The spatial-temporal changes of herding behavior between mutual funds presented in this paper shed light on the debate of individual versus systemic risk and,thus,could interest regulators and investors.
