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Core Determining Class and Inequality Selection
Author(s) -
Ye Luo,
Hai Wang
Publication year - 2017
Publication title -
american economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.936
H-Index - 297
eISSN - 1944-7981
pISSN - 0002-8282
DOI - 10.1257/aer.p20171041
Subject(s) - core (optical fiber) , bipartite graph , inequality , linear inequality , class (philosophy) , mathematical economics , mathematics , graph , set (abstract data type) , selection (genetic algorithm) , measure (data warehouse) , mathematical optimization , computer science , combinatorics , artificial intelligence , data mining , telecommunications , mathematical analysis , programming language
The relations between unobserved events and observed outcomes can be characterized by a bipartite graph. We propose an algorithm that explores the structure of the graph to construct the “exact Core Determining Class,” i.e., the set of irredudant inequalities. We prove that in general the exact Core Determining Class does not depend on the probability measure of the outcomes but only on the structure of the graph. For more general linear inequalities selection problems, we propose a statistical procedure similar to the Dantzig Selector to select the truly informative constraints. We demonstrate performances of our procedures in Monte-Carlo experiments.

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