Jones, M. C.; Signorini, D. F. and Hjort, N. L.
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Hjort and Glad (1995) present a method for semiparametric density estima tion. Relative to the ordinary kernel density estimator, this technique performs much better when a parametric vehicle distribution fits the data, and otherwise performs at broadly the same level. Jones, Linton and Nielsen (1995) present a somewhat similar method for density estimation which has higher order bias for all sufficiently smooth densities. In this paper, we combine the two methods. We show that, theoretically, the desired properties of general higher order bias allied with even better performance for an appropriate vehicle model are achieved. Simulations suggest that the new estimator realises only a little of its theoretical potential in practice for small to moderately large sample sizes.
|Item Type:||Journal Article|
|Copyright Holders:||1999 Indian Statistical Institute|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||26 Apr 2011 11:44|
|Last Modified:||04 Oct 2016 10:46|
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