Abstract
In the context of the non-iterative procedures for performing a correspondence analysis with linear constraints, a new approach is proposed to impose linear constraints in analyzing a contingency table with one ordered set of categories. At the heart of the approach is the partition of the Taguchi’s statistic which has been introduced in the literature as simple alternative to Pearson’s index for contingency tables with an ordered categorical variable. It considers the cumulative frequency of cells in the contingency tables across the ordered variable. Linear constraints are then included directly in suitable matrices reflecting the most important components, overcoming also the problem of imposing linear constraints based on subjective decisions.
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Amenta, P., D’Ambra, A., D’Ambra, L. (2021). Restricted Cumulative Correspondence Analysis. In: Mariani, P., Zenga, M. (eds) Data Science and Social Research II. DSSR 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-51222-4_2
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