Impact Of Data Structure Research Articles

The impact of Data structure on classification ability of financial failure prediction model

The creation of prediction models to reveal the threat of financial difficulties of the companies is realized by the application of various multivariate statistical methods. From a global perspective, prediction models serve to classify a company into a group of prosperous or non-prosperous companies, or to quantify the probability of financial difficulties in the company. In many countries around the world, real financial data about the companies are used in developing these prediction models. In Slovakia, standard data from the financial statements and annual reports of Slovak companies are used for the creation of the company’s failure model. Since in this case there are generally large data files, it is necessary to pre-process the data by the selected methods before the prediction model is constructed. A database of the companies needs to be prepared for the subsequent application of statistical methods, and it is also highly appropriate to focus globally on the detection of potential extreme and remote observations. Therefore, the article will focus on quantifying the impact of the data structure detected, for example, the occurrence of extreme and remote observations in the data set, on the resulting overall classification of the prediction ability of the models created.

The impact of data structure on genetic (co)variance components of early growth in sheep, estimated using an animal model with maternal effects.

Several studies have noted high negative correlations between maternal genetic and direct additive effects and their influence on additive and maternal heritability of early growth traits in sheep. Multigeneration data from the Suffolk Sire Reference Scheme (SSRS) were used to investigate the effect of data structure on estimates of direct and maternal (co)variances for lamb 8-wk weight. In all analyses the additive, maternal genetic, maternal environmental, and residual effects were fitted along with the covariance between direct and maternal additive effects. The contributions of particular genetic relationships to the estimates were studied by analyzing subsets of the SSRS data. A further eight subsets were formed having 10% or 50% of the dams with their own records and having one or two, three or four, five or six, and more than six offspring per dam. Analysis of data having only 10% of the dams with their own record and one or two offspring records yielded a high negative correlation (-0.99) between direct and maternal genetic effects. However, the seven other data sets with more records per dam or a higher proportion of dams with their own records produced values of -0.35 to -0.51. Data structure and the number of dams and granddams with records are important determinants of estimated direct and maternal effects in early growth traits.