Article ID Journal Published Year Pages File Type
11426479 Journal of Phonetics 2018 19 Pages PDF
Abstract
When multiple correlated predictors are considered jointly in regression modeling, estimated coefficients may assume counterintuitive and theoretically uninterpretable values. We survey several statistical methods that implement strategies for the analysis of collinear data: regression with regularization (the elastic net), supervised component generalized linear regression, and random forests. Methods are illustrated for a data set with a wide range of predictors for segment duration in a German speech corpus. Results broadly converge, but each method has its own strengths and weaknesses. Jointly, they provide the analyst with somewhat different but complementary perspectives on the structure of collinear data.
Related Topics
Social Sciences and Humanities Arts and Humanities Language and Linguistics
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