A nominal association matrix with feature selection for categorical data

Wenxue Huang, Yong Shi and Xiaogang Wang

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 16, 7798-7819

Abstract: An intrinsic association matrix is introduced to measure category-to-variable association based on proportional reduction of prediction error by an explanatory variable. The normalization of the diagonal gives rise to the expected rates of error-reduction and the off-diagonal yields expected distributions of the rates of error for all response categories. A general framework of association measures based on the proposed matrix is established using an application-specific weight vector. A hierarchy of equivalence relations defined by the association matrix and vector is shown. Applications to financial and survey data together with simulation results are presented.

Date: 2017
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DOI: 10.1080/03610926.2014.930911

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