Abstract: A kernel density estimator is proposed when tile data are subject to censorship in multivariate case. The asymptotic normality, strong convergence and asymptotic optimal bandwidth which minimize the mean square error of the estimator are studied.

Key words: Kernel density estimator, asymptotic normality, product-limit estimator, mean square error and censored data