  Although the leave-subject-out cross-validation (CV) has been widely used in
practice for tuning parameter selection for various nonparametric and
semiparametric models of longitudinal data, its theoretical property is unknown
and solving the associated optimization problem is computationally expensive,
especially when there are multiple tuning parameters. In this paper, by
focusing on the penalized spline method, we show that the leave-subject-out CV
is optimal in the sense that it is asymptotically equivalent to the empirical
squared error loss function minimization. An efficient Newton-type algorithm is
developed to compute the penalty parameters that optimize the CV criterion.
Simulated and real data are used to demonstrate the effectiveness of the
leave-subject-out CV in selecting both the penalty parameters and the working
correlation matrix.
