Abstract Modern approaches for the spatial simulation of categorical variables are largely based on multi-point statistical methods, where a training image is used to derive complex relationships using relevant patterns. In these approaches, simulated realizations driven by utilized, while statistics actual sample data ignored. This paper presents data-driven, high-order approach approximation ...

Covariances from categorical variables are defined using a regular simplex expression for categories. The method follows the variance definition by Gini, and it gives the covariance as a solution of simultaneous equations. The calculated results give reasonable values for test data. A method of principal component analysis (RS-PCA) is also proposed using regular simplex expressions, which allow...

In analyses of contingency tables made up of categorical variables, the study of relationship between the variables is usually the major objective. So far, many association measures and association models have been used to measure  the association structure present in the table. Although the association measures merely determine the degree of strength of association between the study varia...

In supervised machine learning, the partitioning of the values (also called grouping) of a categorical attribute aims at constructing a new synthetic attribute which keeps the information of the initial attribute and reduces the number of its values. In case of very large number of values, the risk of overfitting the data increases sharply and building good groupings becomes difficult. In this ...

Introduction Manual rating of specific risks begin with a base rate, which is then modified by appropriate relativity factors depending on characteristics of each risk. Classical methods of deriving indicated relativities, are described by McClenahan (1996) and Finger (1996). A number of different modeling procedures are described in Brown's (1988) "minimum bias" paper and Venter's (1990) revie...