A Functional Generalized Linear Model with Curve Selection in Cervical Pre-cancer Diagnosis Using Fluorescence Spectroscopy

A functional generalized linear model is applied to spectroscopic data to discriminate disease from non-disease in the diagnosis of cervical precancer. For each observation, multiple functional covariates are available, and it is of interest to select a few of them for efficient classification. In a...

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Bibliographic Details
Published inLecture notes-monograph series Vol. 57; pp. 173 - 189
Main Authors Zhu, Hongxiao, Cox, Dennis D.
Format Journal Article
LanguageEnglish
Published Institute of Mathematical Statistics 01.01.2009
Subjects
Coefficients
Cosine function
Fluorescence
Generalized linear model
Logistic regression
Modeling
Regression coefficients
Spectroscopy
Wave excitation
Wavelengths
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Summary:A functional generalized linear model is applied to spectroscopic data to discriminate disease from non-disease in the diagnosis of cervical precancer. For each observation, multiple functional covariates are available, and it is of interest to select a few of them for efficient classification. In addition to multiple functional covariates, some non-functional covariates are also used to account for systematic differences caused by these covariates. Functional principal components are used to reduce the model to multivariate logistic regression and a grouped Lasso penalty is applied to the reduced model to select useful functional covariates among multiple curves.
ISSN:0749-2170
2328-3874