Besbeas, P. and Morgan, B.J.T. (2004) Integrated squared error estimation of normal mixtures. Computational Statistics & Data Analysis, 44 (3). pp. 517-526. ISSN 0167-9473 .
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Based on the empirical characteristic function, the integrated squared error criterion for normal mixtures is shown to have a simple form for a particular weight function. When the parameter of that function is chosen as the smoothed cross-validation selector in kernel density estimation, the estimator which minimises the criterion is shown to perform well in a simulation study. In comparison with maximum likelihood and a new recently proposed method there are better bias and standard deviation results for the method of this paper. Furthermore, the new estimator is less likely to fail and is appreciably more robust.
|Uncontrolled keywords:||characteristic function; integrated squared error; Kernel density estimation; normal mixtures; Parseval's theorem; simulated annealing; smoothed cross-validation selector|
|Subjects:||Q Science > QA Mathematics (inc Computing science)|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Judith Broom|
|Date Deposited:||10 Sep 2008 14:59|
|Last Modified:||14 Jan 2010 14:25|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/6756 (The current URI for this page, for reference purposes)|
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