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Functional logistic regression: a comparison of three methods. (English) Zbl 07192551

Summary: Functional logistic regression is becoming more popular as there are many situations where we are interested in the relation between functional covariates (as input) and a binary response (as output). Several approaches have been advocated, and this paper goes into detail about three of them: dimension reduction via functional principal component analysis, penalized functional regression, and wavelet expansions in combination with Least Absolute Shrinking and Selection Operator penalization. We discuss the performance of the three methods on simulated data and also apply the methods to data regarding lameness detection for horses. Emphasis is on classification performance, but we also discuss estimation of the unknown parameter function.

MSC:

62-07 Data analysis (statistics) (MSC2010)
62J12 Generalized linear models (logistic models)
62J07 Ridge regression; shrinkage estimators (Lasso)
62P10 Applications of statistics to biology and medical sciences; meta analysis

Software:

SemiPar; fda (R); refund
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Full Text: DOI

References:

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