Bivariate Density Estimation with Randomly Truncated Data

Ülkü Gürler and Kathryn Prewitt

Journal of Multivariate Analysis, 2000, vol. 74, issue 1, 88-115

Abstract: In this study bivariate kernel density estimators are considered when a component is subject to random truncation. In bivariate truncation models one observes the i.i.d. samples from the triplets (T, Y, X) only if T[less-than-or-equals, slant]Y. In this set-up, Y is said to be left truncated by T and T is right truncated by Y. We consider the estimation of the bivariate density function of (Y, X) via nonparametric kernel methods where Y is the variable of interest and X a covariate. We establish an i.i.d. representation of the bivariate distribution function estimator and show that the remainder term achieves an improved order of O(n-1 ln n), which is desirable for density estimation purposes. Expressions are then provided for the bias and the variance of the estimators. Finally some simulation results are presented.

Keywords: bivariate distribution; truncation/censoring; kernel density estimators (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (2)

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