Statisticians are aware of the fact that measures such as: mean, variance, Pearson correlation coefficient are disproportionately influenced by relatively few extremely large observations and, therefore, are unreliable as statistical measures in comparing overall quality of data with an extremely skewed distribution. Tabular data cells follow an extremely skewed distribution. In this paper we s...

BACKGROUND The network approach to psychopathology conceives mental disorders as sets of symptoms causally impacting on each other. The strengths of the connections between symptoms are key elements in the description of those symptom networks. Typically, the connections are analysed as linear associations (i.e., correlations or regression coefficients). However, there is insufficient awareness...

Let (X 1 ; X 2) be an-stable random vector, not necessarily symmetric, with 0 < < 2. The paper investigates the regression E(X 2 jX 1 = x) for all values of. We give conditions for the existence of the conditional moment E(jX 2 j p jX 1 = x) when p , and we obtain an explicit form of the regression E(X 2 jX 1 = x) as a function of x. Although this regression is, in general, not linear, it can b...

This paper conducts experiments with three skewed data sets, seeking to demonstrate problems when skewed data is used, and identifying counter problems when data is balanced. The basic data mining algorithms of decision tree, regression-based, and neural network models are considered, using both categorical and continuous data. Two of the data sets have binary outcomes, while the third has a se...

BackgroundWhile dealing with skewed outcome, researchers often use log-transformation to convert the data into normal and apply commonly used statistical tests like t-test, linear regression, etc. However, log-transformed will not be at all times. In such situations, Box-Cox transformation (BCT) can transform normal. problem arises when researcher wanted predict outcome in original scale. There...