In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm ...In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm can reduce cases greatly and can preserve competence of CBR system.展开更多
In linear regression analysis, detecting anomalous observations is an important step for model building process. Various influential measures based on different motivational arguments and designed to measure the influ...In linear regression analysis, detecting anomalous observations is an important step for model building process. Various influential measures based on different motivational arguments and designed to measure the influence of observations on different aspects of various regression results are elucidated and critiqued. The presence of influential observations in the data is complicated by the presence of multicollinearity. In this paper, when Liu estimator is used to mitigate the effects of multicollinearity the influence of some observations can be drastically modified. Approximate deletion formulas for the detection of influential points are proposed for Liu estimator. Two real macroeconomic data sets are used to illustrate the methodologies proposed in this paper.展开更多
Evaluating cloud seeding effects is one of the most critical issues in artificial precipitation enhancement experiments. However, the evaluation is not straightforward because there is natural rainfall variability, wh...Evaluating cloud seeding effects is one of the most critical issues in artificial precipitation enhancement experiments. However, the evaluation is not straightforward because there is natural rainfall variability, which subjects the atmosphere to spatiotemporal instabilities. The aim of this study is to analyze natural rainfall variability using the modern statistical simulation method, "bootstrap", to analyze its influence on the evaluation of seeding activities and to take proper measures to control the influence. The study is based on the 1997?2007 airborne seeding macro records and the daily precipitation data in Jilin Province. The influence of natural rainfall variability can be reduced through three approaches: the increase of the supposed "seeded" sample size N, the rejection of outliers, and the selection of similar control units. A larger N leads to smaller calculated precipitation variability and detectable lower limits of seeding effects. When N is near 470 and the seeding effect is between 20% and 30%, the confidence level reaches 90%. For a single seeding operation, the case deletion model that rejects strong influence points and selects similar control units is established to control the influence of natural precipitation variability, which obviously improves the evaluation of artificial precipitation enhancement. The results demonstrate that the relative seeding effect in Jilin Province is concentrated mainly in the range of 0 to 30%, with an average of 11.95%, and has no significant linear relationship with the actual precipitation amount. However, the fluctuation amplitude of the relative effect decreases as the precipitation amount rises.展开更多
Dear Editor,I am Dr.Jia X from the Department of Ophthalmology,Second Xiangya Hospital,Central South University,Changsha,China.I write to present a rare case report of 9p deletion syndrome with congenital infantile gl...Dear Editor,I am Dr.Jia X from the Department of Ophthalmology,Second Xiangya Hospital,Central South University,Changsha,China.I write to present a rare case report of 9p deletion syndrome with congenital infantile glaucoma in an infant,accompanying with an effective method of both diagnosis and treatment.展开更多
In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood e...In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.展开更多
文摘In this paper, a new Case-Deletion strategy is proposed. This method absorbs merits o[ clustering algorithm. It overcomes the deflection of traditional deletion strategies. The experiments show that the new algorithm can reduce cases greatly and can preserve competence of CBR system.
文摘In linear regression analysis, detecting anomalous observations is an important step for model building process. Various influential measures based on different motivational arguments and designed to measure the influence of observations on different aspects of various regression results are elucidated and critiqued. The presence of influential observations in the data is complicated by the presence of multicollinearity. In this paper, when Liu estimator is used to mitigate the effects of multicollinearity the influence of some observations can be drastically modified. Approximate deletion formulas for the detection of influential points are proposed for Liu estimator. Two real macroeconomic data sets are used to illustrate the methodologies proposed in this paper.
基金supported by the National Meteorological Public Benefit Research Foundation(Grant No.GYHY201006031)the China Meteorological Administration Soft Science Project(Grant No.2012-053)+1 种基金the Jiangsu Province Science Department Grant(Grant No.CB10X_295Z)the Jiangsu Province Qinglan Project for Cloud Fog Precipitation and Aerosol Research Group
文摘Evaluating cloud seeding effects is one of the most critical issues in artificial precipitation enhancement experiments. However, the evaluation is not straightforward because there is natural rainfall variability, which subjects the atmosphere to spatiotemporal instabilities. The aim of this study is to analyze natural rainfall variability using the modern statistical simulation method, "bootstrap", to analyze its influence on the evaluation of seeding activities and to take proper measures to control the influence. The study is based on the 1997?2007 airborne seeding macro records and the daily precipitation data in Jilin Province. The influence of natural rainfall variability can be reduced through three approaches: the increase of the supposed "seeded" sample size N, the rejection of outliers, and the selection of similar control units. A larger N leads to smaller calculated precipitation variability and detectable lower limits of seeding effects. When N is near 470 and the seeding effect is between 20% and 30%, the confidence level reaches 90%. For a single seeding operation, the case deletion model that rejects strong influence points and selects similar control units is established to control the influence of natural precipitation variability, which obviously improves the evaluation of artificial precipitation enhancement. The results demonstrate that the relative seeding effect in Jilin Province is concentrated mainly in the range of 0 to 30%, with an average of 11.95%, and has no significant linear relationship with the actual precipitation amount. However, the fluctuation amplitude of the relative effect decreases as the precipitation amount rises.
基金Supported by the Natural Science Foundation of China(No.81370913)
文摘Dear Editor,I am Dr.Jia X from the Department of Ophthalmology,Second Xiangya Hospital,Central South University,Changsha,China.I write to present a rare case report of 9p deletion syndrome with congenital infantile glaucoma in an infant,accompanying with an effective method of both diagnosis and treatment.
基金The National Natural Science Foundation of China(No.11171065)the Natural Science Foundation of Jiangsu Province(No.BK2011058)
文摘In order to detect whether the data conforms to the given model, it is necessary to diagnose the data in the statistical way. The diagnostic problem in generalized nonlinear models based on the maximum Lq-likelihood estimation is considered. Three diagnostic statistics are used to detect whether the outliers exist in the data set. Simulation results show that when the sample size is small, the values of diagnostic statistics based on the maximum Lq-likelihood estimation are greater than the values based on the maximum likelihood estimation. As the sample size increases, the difference between the values of the diagnostic statistics based on two estimation methods diminishes gradually. It means that the outliers can be distinguished easier through the maximum Lq-likelihood method than those through the maximum likelihood estimation method.
