The use of [1] Box-Cox power transformation in regression analysis is now common;in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved dele...The use of [1] Box-Cox power transformation in regression analysis is now common;in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.展开更多
Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering th...Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering the model assumption of observation process,a more general semiparametric transformation model for panel count data with informative observation process is developed.A penalized estimation procedure based on the quantile regression function is proposed for variable selection and parameter estimation simultaneously.The consistency and oracle properties of the estimators are established under some mild conditions.Some simulations and an application are reported to evaluate the proposed approach.展开更多
Increasing air temperatures are expected to continue in the future. The relation between soil moisture and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relatio...Increasing air temperatures are expected to continue in the future. The relation between soil moisture and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relations between soil moisture and temperature was performed by developing a quantile regression model, a wavelet coherency model, and a Mann-Kendall correlation model from 1950 to 2010 in the Mississippi River Basin. The results indicate that first, anomaly air temperature is negatively correlated to anomaly soil moisture in the upper and lower basin, and however, the correlation between them are mixed in the middle basin. The correlation is stronger at the higher quantile (90th) of the two variables. Second, anomaly soil moisture and air temperature show strong coherency in annual frequency, indicating that the two variables are interannually correlated. Third, annual air temperature is significant negatively related to soil moisture, indicating that dry (wet) soil leads to warm (cool) weather in the basin. These results have potential application to future climate change research and water resource management. Also, the strong relationship between soil moisture and air temperature at annual scale could result in improved temperature predictability.展开更多
文摘The use of [1] Box-Cox power transformation in regression analysis is now common;in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.
基金partially supported by the National Natural Science Foundation of China under Grant No.12001485the National Bureau of Statistics of China under Grant No.2020LY073the First Class Discipline of Zhejiang-A(Zhejiang University of Finance and Economics-Statistics)under Grant No.Z0111119010/024。
文摘Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering the model assumption of observation process,a more general semiparametric transformation model for panel count data with informative observation process is developed.A penalized estimation procedure based on the quantile regression function is proposed for variable selection and parameter estimation simultaneously.The consistency and oracle properties of the estimators are established under some mild conditions.Some simulations and an application are reported to evaluate the proposed approach.
文摘Increasing air temperatures are expected to continue in the future. The relation between soil moisture and near surface air temperature is significant for climate change and climate extremes. Evaluation of the relations between soil moisture and temperature was performed by developing a quantile regression model, a wavelet coherency model, and a Mann-Kendall correlation model from 1950 to 2010 in the Mississippi River Basin. The results indicate that first, anomaly air temperature is negatively correlated to anomaly soil moisture in the upper and lower basin, and however, the correlation between them are mixed in the middle basin. The correlation is stronger at the higher quantile (90th) of the two variables. Second, anomaly soil moisture and air temperature show strong coherency in annual frequency, indicating that the two variables are interannually correlated. Third, annual air temperature is significant negatively related to soil moisture, indicating that dry (wet) soil leads to warm (cool) weather in the basin. These results have potential application to future climate change research and water resource management. Also, the strong relationship between soil moisture and air temperature at annual scale could result in improved temperature predictability.
