Consumer finance data generator - a new approach to Credit Scoring technique comparison
This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data from another source. The following research challenge can therefore be formulated: how should the comparison be managed in order to get general results that are not biased by particular data? The solution may be in the use of various random data generators. The data generator uses two approaches: transition matrix and scorings. Here are presented both: results of comparison methods and the methodology of these comparison techniques creating. Before building a new model the modeler can undertake a comparison exercise that aims at identifying the best method in the case of the particular data. Here are presented various measures of predictive model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the multi-criteria problem of a "Good model". The idea that is being suggested is of particular use in the model building process where there are defined complex criteria trying to cover the important problems of model stability over a period of time, in order to avoid a crisis. Some arguments for choosing Logit or WOE approach as the best scorecard technique are presented.
Regressions by Leaps and Bounds
This paper describes several algorithms for computing the residual sums of squares for all possible regressions with what appears to be a minimum of arithmetic (less than six floating-point operations per regression) and shows how two of these algorithms can be combined to form a simple leap and bound technique for finding the best subsets without examining all possible subsets. The result is a reduction of several orders of magnitude in the number of operations required to find the best subsets.
Influence of filler addition, storage medium and evaluation time on biaxial flexure strength and modulus of adhesive systems
Abstract Objective. This study evaluated the effects of filler addition, storage medium and time on biaxial flexural strength and flexural modulus of six adhesive systems. Materials and methods. The adhesives were either unfilled resins: Single Bond, Prime&Bond 2.1 and One-Step; or filled resins: Single Bond Plus, Prime&Bond NT and One-Step Plus. Resin discs of each product (0.5 mm thick × 6.0 mm diameter) were prepared using silicon molds (n = 10). The discs were stored dry, in water, mineral oil or ethanol for 1 week or 3 months before biaxial flexural testing. Data were statistically analyzed by 3-way ANOVA and Tukey's post-hoc test (α = 0.05). The specimens were tested in a universal testing machine at 1.27 mm/min until failure occurred. Results. In general, the storage in ethanol led to significantly lower flexural strength and flexural modulus values than the other storage conditions for all adhesives in both storage periods. Filler addition increased flexural strength and flexural modulus for Prime&Bond NT, when it was stored dry or in water for 1 week. For Single Bond Plus, the filler addition resulted in higher flexural modulus, when it was stored for 1 week in oil and in ethanol. No significant differences in flexural modulus were observed between One-Step and One-Step Plus in any storage medium. Conclusions. Results suggested that filler addition does not necessarily have to increase the flexural strength and flexural modulus. Ethanol and oil storages tended to yield opposite effects.