Analysis of mixed data: methods & applications by Alexander R. de Leon, Keumhee Carrière Chough PDF

By Alexander R. de Leon, Keumhee Carrière Chough

ISBN-10: 1439884714

ISBN-13: 9781439884713

"A entire resource on combined info research, research of combined facts: equipment & functions summarizes the elemental advancements within the box. Case reviews are used broadly in the course of the booklet to demonstrate attention-grabbing functions from economics, drugs and overall healthiness, advertising and marketing, and genetics. rigorously edited for gentle clarity and seamless transitions among chaptersAll chapters stick with a common Read more...

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Extra info for Analysis of mixed data: methods & applications

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In the multivariate linear regression context, methods with better predictive performance than the usual multivariate ordinary least squares (OLS) regression, which amounts to fitting separately an OLS regression model to each outcome, have been proposed (see for example, Breiman and Friedman, 1997). , 2010, and Koch and Naito, 2010). Hence, multivariate models can be useful for the sole purpose of predictions, and not only to handle the multiple testing problem. , 1984) are valuable alternatives to parametric methods.

In many applications, sparseness of data becomes a vexing concern for such models, especially in high-dimensional situations. 3), which may prohibit their adoption in practice. Nevertheless, the literature offers a variety of approaches for model estimation. Given a full specification of the model, a likelihood-based approach is an obvious choice; however, evaluation and direct maximization of the full likelihood frequently lack an analytical solution. Approximations, mostly numerical, have thus been proposed in the literature.

The classical tree-based methods can accommodate one continuous or one categorical outcome at a time. When multiple outcomes are available, one possibility is to grow as many univariate trees as there are outcomes. But understanding the overall structure by inspecting many trees might be difficult. Hence, it may be preferable to build a single multivariate tree for all outcomes, since it may provide a parsimonious and simple-to-interpret model. Few generalizations of tree-based methods to multiple outcomes have been proposed.

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Analysis of mixed data: methods & applications by Alexander R. de Leon, Keumhee Carrière Chough


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