When the variable of model is large, the Lasso method and the Adaptive Lasso method can effectively select variables. This paper prediction the rural residents’ consumption expenditure in China, based on respectively...When the variable of model is large, the Lasso method and the Adaptive Lasso method can effectively select variables. This paper prediction the rural residents’ consumption expenditure in China, based on respectively using the Lasso method and the Adaptive Lasso method. The results showed that both can effectively and accurately choose the appropriate variable, but the Adaptive Lasso method is better than the Lasso method in prediction accuracy and prediction error. It shows that in variable selection and parameter estimation, Adaptive Lasso method is better than the Lasso method.展开更多
This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model.The research integrated the adoption of the...This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model.The research integrated the adoption of the innovation framework and the technology acceptance theory to develop a conceptual framework for the analysis.Primary data was collected from 400 bank customers in North Cyprus.Risk perception and other customerspecific factors such as perceived risk index and negative attitude toward new technologies index were formulated for the proposed conceptual model.The findings indicated that individuals with a negative attitude toward new technology are least likely to adopt internet banking.In addition,the logit model suggested that age,education level,and general(innate)innovativeness significantly impact the adoption of internet banking.However,gender,income,occupation,perceived risk,familiarity with the internet,and social inclusion have no significant impact on internet banking adoption in North Cyprus.展开更多
To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is anal...To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.展开更多
We study the asymptotic properties of adaptive lasso estimators when some components of the parameter of interest βare strictly different than zero, while other components may be zero or may converge to zero ...We study the asymptotic properties of adaptive lasso estimators when some components of the parameter of interest βare strictly different than zero, while other components may be zero or may converge to zero with rate n<sup>-δ</sup>, with δ>0, where n denotes the sample size. To achieve this objective, we analyze the convergence/divergence rates of each term in the first-order conditions of adaptive lasso estimators. First, we derive conditions that allow selecting tuning parameters in order to ensure that adaptive lasso estimates of n<sup>-δ</sup>-components indeed collapse to zero. Second, in this case, we also derive asymptotic distributions of adaptive lasso estimators for nonzero components. When δ>1/2, we obtain the usual n<sup>1/2</sup>-asymptotic normal distribution, while when 0δ≤1/2, we show n<sup>δ</sup>-consistency combined with (biased) n<sup>1/2-δ</sup>-asymptotic normality for nonzero components. We call these properties, Extended Oracle Properties. These results allow practitioners to exclude in their model the asymptotically negligible variables and make inferences on the asymptotically relevant variables.展开更多
In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 penalty. The calculation methods of the proposed estimators are given on t...In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO(aL ASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models.展开更多
In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average varianc...In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.展开更多
文摘When the variable of model is large, the Lasso method and the Adaptive Lasso method can effectively select variables. This paper prediction the rural residents’ consumption expenditure in China, based on respectively using the Lasso method and the Adaptive Lasso method. The results showed that both can effectively and accurately choose the appropriate variable, but the Adaptive Lasso method is better than the Lasso method in prediction accuracy and prediction error. It shows that in variable selection and parameter estimation, Adaptive Lasso method is better than the Lasso method.
文摘This research investigates a broad range of possible factors affecting the adoption of new technology in the banking industry using adaptive LASSO and a standard logit model.The research integrated the adoption of the innovation framework and the technology acceptance theory to develop a conceptual framework for the analysis.Primary data was collected from 400 bank customers in North Cyprus.Risk perception and other customerspecific factors such as perceived risk index and negative attitude toward new technologies index were formulated for the proposed conceptual model.The findings indicated that individuals with a negative attitude toward new technology are least likely to adopt internet banking.In addition,the logit model suggested that age,education level,and general(innate)innovativeness significantly impact the adoption of internet banking.However,gender,income,occupation,perceived risk,familiarity with the internet,and social inclusion have no significant impact on internet banking adoption in North Cyprus.
基金This work was supported in part by the National Key R&D Program of China(No.2019YFE0122600),author H.H,https://service.most.gov.cn/in part by the Project of Centre for Innovation Research in Social Governance of Changsha University of Science and Technology(No.2017ZXB07),author J.H,https://www.csust.edu.cn/mksxy/yjjd/shzlcxyjzx.htm+2 种基金in part by the Public Relations Project of Philosophy and Social Science Research Project of the Ministry of Education(No.17JZD022),author J.L,http://www.moe.gov.cn/in part by the Key Scientific Research Projects of Hunan Provincial Department of Education(No.19A015),author J.L,http://jyt.hunan.gov.cn/in part by the Hunan 13th five-year Education Planning Project(No.XJK19CGD011),author J.H,http://ghkt.hntky.com/.
文摘To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.
文摘We study the asymptotic properties of adaptive lasso estimators when some components of the parameter of interest βare strictly different than zero, while other components may be zero or may converge to zero with rate n<sup>-δ</sup>, with δ>0, where n denotes the sample size. To achieve this objective, we analyze the convergence/divergence rates of each term in the first-order conditions of adaptive lasso estimators. First, we derive conditions that allow selecting tuning parameters in order to ensure that adaptive lasso estimates of n<sup>-δ</sup>-components indeed collapse to zero. Second, in this case, we also derive asymptotic distributions of adaptive lasso estimators for nonzero components. When δ>1/2, we obtain the usual n<sup>1/2</sup>-asymptotic normal distribution, while when 0δ≤1/2, we show n<sup>δ</sup>-consistency combined with (biased) n<sup>1/2-δ</sup>-asymptotic normality for nonzero components. We call these properties, Extended Oracle Properties. These results allow practitioners to exclude in their model the asymptotically negligible variables and make inferences on the asymptotically relevant variables.
基金supported by the National Natural Science Foundation of China under Grant No.61272041
文摘In this paper, based on spline approximation, the authors propose a unified variable selection approach for single-index model via adaptive L1 penalty. The calculation methods of the proposed estimators are given on the basis of the known lars algorithm. Under some regular conditions, the authors demonstrate the asymptotic properties of the proposed estimators and the oracle properties of adaptive LASSO(aL ASSO) variable selection. Simulations are used to investigate the performances of the proposed estimator and illustrate that it is effective for simultaneous variable selection as well as estimation of the single-index models.
文摘In this article, we study the variable selection of partially linear single-index model(PLSIM). Based on the minimized average variance estimation, the variable selection of PLSIM is done by minimizing average variance with adaptive l1 penalty. Implementation algorithm is given. Under some regular conditions, we demonstrate the oracle properties of aLASSO procedure for PLSIM. Simulations are used to investigate the effectiveness of the proposed method for variable selection of PLSIM.
