Summary
The aim of Configural Frequency Analysis (CFA) is the search for outliers or ‘types’ (subdivided into ‘types’ and ‘antitypes’) in a sample of d-dimensional finite vectors, generally represented in a d-dimensional contingency table. Type search is done by analysis of residuals. It can be shown, however, that this technique may be misleading. The use of interpolated (deleted) residuals and/or other techniques will give better results. Deletion of entries results in incomplete tables. Expected values can be computed with the aid of Iterative Proportional Fitting (IPF). The analysis of logarithmic expectations leads to equation systems similar to those occurring in log-linear models. There is no restriction to the independence model assumed in CFA. The Markov chain as example of a more general but still simple model is treated in this paper.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Ihm, P., Küchler, I. (1996). Alternatives to Configural Frequency Analysis. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_18
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DOI: https://doi.org/10.1007/978-3-642-79999-0_18
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