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
The primal-dual hybrid gradient method is a classic way to tackle saddle-point problems.However,its convergence is not guaranteed in general.Some restric-tions on the step size parameters,e.g.,τσ≤1/||A^(T)A||,are i...The primal-dual hybrid gradient method is a classic way to tackle saddle-point problems.However,its convergence is not guaranteed in general.Some restric-tions on the step size parameters,e.g.,τσ≤1/||A^(T)A||,are imposed to guarantee the convergence.In this paper,a new convergent method with no restriction on parame-ters is proposed.Hence the expensive calculation of ||A^(T)A|| is avoided.This method produces a predictor like other primal-dual methods but in a parallel fashion,which has the potential to speed up the method.This new iterate is then updated by a sim-ple correction to guarantee the convergence.Moreover,the parameters are adjusted dynamically to enhance the efficiency as well as the robustness of the method.The generated sequence monotonically converges to the solution set.A worst-case O(1/t)convergence rate in ergodic sense is also established under mild assumptions.The nu-merical efficiency of the proposed method is verified by applications in LASSO problem and Steiner tree problem.展开更多
Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to...Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to be heteroscedastic,while in composite quantile regression,we require that the noise process be homoscedastic so that the slopes are invariant across quantiles.Similar to the independent noise case,the proposed quantile estimators are root-n consistent and asymptotic normal.Furthermore,the adaptive least absolute shrinkage and selection operator(LASSO)is applied for the purpose of variable selection.As a result,the quantile estimators are consistent in variable selection,and the nonzero coefficient estimators enjoy the same asymptotic distribution as their counterparts under the true model.Extensive numerical simulations are conducted to evaluate the performance of the proposed approaches and foreign exchange rate data are analyzed for the illustration purpose.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 research is supported by National Natural Science Foundation of China(Nos.71201080,71571096)Social Science Foundation of Jiang-su Province(No.14GLC001)Fundamental Research Funds for the Central Universities(No.020314380016).
文摘The primal-dual hybrid gradient method is a classic way to tackle saddle-point problems.However,its convergence is not guaranteed in general.Some restric-tions on the step size parameters,e.g.,τσ≤1/||A^(T)A||,are imposed to guarantee the convergence.In this paper,a new convergent method with no restriction on parame-ters is proposed.Hence the expensive calculation of ||A^(T)A|| is avoided.This method produces a predictor like other primal-dual methods but in a parallel fashion,which has the potential to speed up the method.This new iterate is then updated by a sim-ple correction to guarantee the convergence.Moreover,the parameters are adjusted dynamically to enhance the efficiency as well as the robustness of the method.The generated sequence monotonically converges to the solution set.A worst-case O(1/t)convergence rate in ergodic sense is also established under mild assumptions.The nu-merical efficiency of the proposed method is verified by applications in LASSO problem and Steiner tree problem.
基金supported by National Natural Science Foundation of China(Grant Nos.11801355 and 11971116).
文摘Based on the data-cutoff method,we study quantile regression in linear models,where the noise process is of Ornstein-Uhlenbeck type with possible jumps.In single-level quantile regression,we allow the noise process to be heteroscedastic,while in composite quantile regression,we require that the noise process be homoscedastic so that the slopes are invariant across quantiles.Similar to the independent noise case,the proposed quantile estimators are root-n consistent and asymptotic normal.Furthermore,the adaptive least absolute shrinkage and selection operator(LASSO)is applied for the purpose of variable selection.As a result,the quantile estimators are consistent in variable selection,and the nonzero coefficient estimators enjoy the same asymptotic distribution as their counterparts under the true model.Extensive numerical simulations are conducted to evaluate the performance of the proposed approaches and foreign exchange rate data are analyzed for the illustration purpose.
