Density estimation with bivariate censored data

In this article we construct a kernel estimate of the probability density function from bivariate data that have been randomly censored. We study the large-sample properties of the proposed estimator using a strong approximation result. We establish consistency and asymptotic normality and give a convenient representation of the kernel density estimator. Simulation studies show that the proposed procedure gives a good estimate of the true density function even when the sample size is moderate. We discuss various issues about implementation of the estimator, including bandwidth selection and boundary effects. The procedure can be generalized to higher dimensional variables in a straightforward manner.