PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.

Inhibitive effects of three compositae plants on Microcystis aeruginosa

Based on common phenomena of biochemical interaction between plants and microorganisms,the inhibitive effects of three common terrestrial compositae plants,namely Artemisia lavandulaefolia DC.,Conyza canadensis(L.)Cronq.,and Kalimeris indica(L.)Sch.-Bip.on the blue algae Microcystis aeruginosa was studied.Live compositae plants are co-cultivated with algae in two different inoculation doses for 10 days in 5-pools incubators,in order to exclude the influence of bacteria and nutrients.The results show that Artemisia lavandulaefolia DC has the most inhibitive potential among the three plants as evidenced by the most drastic decrease in optical density(OD680)of the algae.The inhibition rate is 93.3%(with initial inoculation dose of 2.0×10^(6) Cells/mL)and 89.3%(with initial inoculation dose of 4.0×10^(6) Cells/mL)respectively on the 10th day of cultivation.The average inhibition rate during the later half of the experiment is 0.76(with initial inoculation dose of 2.0×10^(6) Cells/mL)and 0.71(with initial inoculation dose of 4.0×10^(6) Cells/mL),respectively.Logistic model analysis shows that compositae plants such as A.lavandulaefolia DC.causes the reduction of the habitat’s carrying capacity of algae.ANOVA analysis is used to determine the similarity and differences between every experimental group and an average inhibitive rate model is used to evaluate the inhibition effects.The results show that A.lavandulaefolia DC.,which grow well in the aquatic environment,may have a great potential in controlling algae bloom in eutrophic water.