Estimating a bivariate density when there are extra data on one or both components
The objective of this paper is to estimate a bivariate density nonparametrically from a dataset from the joint distribution and datasets from one or both marginal distributions. We develop a copula-based solution, which has potential benefits even when the marginal datasets are empty. For example, if the copula density is sufficiently smooth in the region where we wish to estimate it, the joint density can be estimated with a high degree of accuracy. Similar improvements in performance are...[Show more]
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