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Boolean analysis is a partial order generalization of scalogram analysis. It enables one to build a family of implication schemes that bears a 1-1-relationship with a data set. Unlike scalogram analysis, this method does not impose any constraints on the data structure. As a result, implication schemes may become extremely complex and may contain errors. Therefore, methods are needed for the selection of subsets of data to be modeled (dichotomization methods). In this chapter, a dichotomization method is introduced which uses the relative frequency of data patterns with the same number of correct responses as a given criterion. It is argued that the present approach yields implication schemes which comply better with the underlying data structure, especially when the subjects show similar abilities in solving the items.
The main purpose of this volume is to give a modern, up-to-date, presentation of the theory of Hopf algebras and their applications, classical and recent. The Hopf algebras we consider are the classical ones: the ones that appeared in the 40s and 50s in algebraic topology, the theory of algebraic groups and representation theory. That is, they will be most often commutative or cocommutative and associated to a group of characters or group-like elements. Concretely, we will consider typically enveloping algebras of graded or complete Lie algebras and their dual Hopf algebras, Hopf algebras of representative functions, of trees and graphs, and similar ones. The account will include certain Hopf algebras carrying extra algebraic structures, such as, for example, enveloping algebras of pre-Lie algebras. Applications developed will include duality phenomena in group theory; classical Hopf algebra structures in algebraic topology; combinatorial Hopf algebras; and Hopf algebraic renormalization. 153554b96e