Institute of Mathematical Statistics Lecture Notes - Monograph Series

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

Hongxiao Zhu and Dennis D. Cox

Full-text: Open access

Abstract

A functional generalized linear model is applied to spectroscopic data to discriminate disease from non-disease in the diagnosis of cervical pre-cancer. 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.

Chapter information

Source
Javier Rojo, ed., Optimality: The Third Erich L. Lehmann Symposium (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009), 173-189

Dates
First available in Project Euclid: 3 August 2009

Permanent link to this document
https://projecteuclid.org/euclid.lnms/1249305329

Digital Object Identifier
doi:10.1214/09-LNMS5711

Mathematical Reviews number (MathSciNet)
MR2681663

Zentralblatt MATH identifier
1271.62091

Subjects
Primary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]
Secondary: 60K37: Processes in random environments

Keywords
functional generalized linear model curve selection grouped lasso fluorescence spectroscopy cervical cancer

Rights
Copyright © 2009, Institute of Mathematical Statistics

Citation

Rojo, Javier. A Functional Generalized Linear Model with Curve Selection in Cervical Pre-cancer Diagnosis Using Fluorescence Spectroscopy. Optimality, 173--189, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2009. doi:10.1214/09-LNMS5711. https://projecteuclid.org/euclid.lnms/1249305329


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