Download 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 information research, research of combined facts: tools & purposes summarizes the elemental advancements within the box. Case reports are used broadly during the publication to demonstrate attention-grabbing functions from economics, drugs and wellbeing and fitness, advertising, and genetics. rigorously edited for tender clarity and seamless transitions among chaptersAll chapters keep on with a common Read more...

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Additional resources for Analysis of mixed data: methods & applications

Sample text

The extension of GCM to the case of mixed discrete and continuous data has been studied by Anderson and Pemberton (1985) and by Poon and Lee (1987, 1986), and is referred to as the conditional GCM (CGCM) in the literature. This approach involves the assumption that the continuous variables share a joint multivariate normal distribution with the latent variables, and the thresholds and polychoric correlations are defined in terms of the conditional distribution of the latent variables (or the discrete data) given the continuous data.

2 Basic tree building The classical way to build a tree is to recursively split the sample in order to partition it into more and more homogeneous nodes. Starting from a root node containing all the data, a best split, defined with the covariates, is found. Even though more complicated splits involving linear combinations are possible, only simple splits involving one predictor are usually considered. For a continuous (or at least ordinal) covariate x, the possible splits take the form x ≤ c, where c is a specified cutpoint.

This particular model assumes a uniform dispersion matrix Σ across the states and is called a homogeneous CGD in the graphical modeling literature. Olkin and Tate (1961), while considering canonical correlations between the binary and continuous variables, established results connecting these canonical correlations and the state means. Another approach to handling mixed data assumes that the discrete variables are coarsely measured versions of unobservable continuous variables called latent variables, and are obtained by partitioning or thresholding the space of the latent variables into non-overlapping intervals.

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