Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators

We propose a classification method for longitudinal data. The Bayes classifier is classically used to determine a classification rule where the underlying density in each class needs to be well modeled and estimated. This work is motivated by a real dataset of hormone levels measured at the early st...

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Bibliographic Details
Published in:Biometrics 2015-06, Vol.71 (2), p.333-343
Main Authors: Arribas-Gil, Ana, De la Cruz, Rolando, Lebarbier, Emilie, Meza, Cristian
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Biometry
Chorionic Gonadotropin, beta Subunit, Human - metabolism
Classification
Computer Simulation
Data Interpretation, Statistical
DISCUSSION PAPER
Economic models
EM algorithm
Female
Humans
Lasso
Life Sciences
Likelihood Functions
Linear Models
Longitudinal data
Longitudinal Studies
Mixed-effects models
Models, Statistical
Nonlinear Dynamics
Pregnancy
Pregnancy Complications - diagnosis
Pregnancy Complications - metabolism
Pregnancy Outcome
Regression analysis
Semiparametric estimation
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Summary:We propose a classification method for longitudinal data. The Bayes classifier is classically used to determine a classification rule where the underlying density in each class needs to be well modeled and estimated. This work is motivated by a real dataset of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. The proposed model, which is a semiparametric linear mixed-effects model (SLMM), is a particular case of the semiparametric nonlinear mixed-effects class of models (SNMM) in which finite dimensional (fixed effects and variance components) and infinite dimensional (an unknown function) parameters have to be estimated. In SNMM's maximum likelihood estimation is performed iteratively alternating parametric and nonparametric procedures. However, if one can make the assumption that the random effects and the unknown function interact in a linear way, more efficient estimation methods can be used. Our contribution is the proposal of a unified estimation procedure based on a penalized EM-type algorithm. The Expectation and Maximization steps are explicit. In this latter step, the unknown function is estimated in a nonparametric fashion using a lasso-type procedure. A simulation study and an application on real data are performed.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12280