  A nonparametric kernel density estimator for directional-linear data is
introduced. The proposal is based on a product kernel accounting for the
different nature of both (directional and linear) components of the random
vector. Expressions for bias, variance and Mean Integrated Squared Error (MISE)
are derived, jointly with an asymptotic normality result for the proposed
estimator. For some particular distributions, an explicit formula for the MISE
is obtained and compared with its asymptotic version, both for directional and
directional-linear kernel density estimators. In this same setting a closed
expression for the bootstrap MISE is also derived.
