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\footnotesize \noindent Statistica Sinica {\bf 24} (2014), 147-171

\footnotesize \noindent

\vspace{3pc} \normalsize \centerline{\large\bf GENERALIZED VARYING
COEFFICIENT MODELS: } \vspace{2pt} \centerline{\large\bf A SMOOTH
VARIABLE SELECTION TECHNIQUE} \vspace{.25cm}
\author{Anneleen Verhasselt}
\vspace{.4cm} \centerline{\it  Universiteit Hasselt} \vspace{.55cm}
\begin{quotation}
\noindent {\it Abstract:} We consider nonparametric smoothing and
variable selection in generalized varying coefficient models.
Generalized varying coefficient models are commonly used for
analyzing the time-dependent effects of covariates on responses that
are not necessary continuous,  for example counts or categories. We
present the P-spline estimator in this context and show its
estimation consistency for a diverging number of knots (or B-spline
basis functions), by using an approximation of the link function.
The combination of P-splines with nonnegative garrote (which is a
variable selection method) leads to good estimation and variable
selection. The method is illustrated with a simulation study and a
data example.\par \vspace{9pt} \noindent {\it Key words and
phrases:} generalized varying coefficient models, longitudinal data,
nonparametric smoothing, P-splines, variable selection.
\par \par
\end{quotation}

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