Tests for multivariate recurrent events in the presence of a by Chen B., Cook R. J.

By Chen B., Cook R. J.

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30. , Sassoli, P. (2001): eBusiness Applications to Flexible Transport and Mobility Services. Eunite 2001. 31. M. (2001): Modeling Investor Reasoning Using Fuzzy Cognitive Maps. Eunite 2001. 32. C. (2002): The VPIS System: A New Approach to Healthcare logistics. Eunite 2002. 26 K. Leivisk¨ a 33. Heikkil¨ a, M. (2002): Knowledge Processing in Real Options. Eunite 2002. 34. Nelke, M. (2001): Supplier Relationship Management: Advanced Vendor Evaluation for Enterprise Resource Planning Systems. Eunite 2001.

The effect of LIL binning is shown in Fig. 7(d) above and results in the transmission of more information through the linear and functional modes than all types of unsupervised binning, and many other types of supervised binning, although Fig. 7(d) shows the effect for the data set as a whole and not just for an individual variable. Binning Summary Binning is a useful and valuable tool in the armory of data preparation. However, the ease of binning is not an unmixed blessing because it is very easy to do more harm than good with binning.

However, that is not the whole story. Usually (invariably in the 3 Data Preparation and Preprocessing 45 author’s experience) nulls do not occur at random in any data set. Indeed, the presence of a null very often depends on the values of the other variables in a data set, so even the presence of a null is sensitive to the multivariate distribution, and this is just as true for categorical variables as much for as numeric variables. Thus, it may be important to explicitly capture the information that could be transmitted by the pattern with which nulls occur in a data set – and it is this pattern that is potentially lost by any tool that ignores missing values.

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