  We consider the problem of estimation of a bivariate density function with
support $\Re\times[0,\infty)$, where a classical bivariate kernel estimator
causes boundary bias due to the non-negative variable. To overcome this
problem, we propose four kernel density estimators and compare their
performances in terms of the mean integrated squared error. Simulation study
shows that the estimator based on the proposed normal-gamma kernel performs
best. Two astronomical data sets are used to demonstrate the applicability of
this estimator.
