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Abstract Details
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Activity Number:
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172
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #301039 |
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Title:
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Improved Polynomially Adjusted Density Estimates
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Author(s):
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Serge B. Provost*+ and Min Jiang
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Companies:
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The University of Western Ontario and Statistics Canada
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Address:
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Dept. of Stat. & Act. Sciences, London, ON, N6A5B7, Canada
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Keywords:
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Density estimation ;
Orthogonal polynomials ;
Degree selection ;
Multivariate distributions
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Abstract:
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Density estimates that are expressible as the product of a base density function and a linear combination of orthogonal polynomials are being considered. More specifically, two criteria are proposed for determining the number of terms to be included in the polynomial adjustment component and guidelines are suggested for the selection of a suitable base density function. A simulation study reveals that these stopping rules produce density estimates that are generally more accurate than kernel density estimates or those resulting from the application of the Kronmal-Tarter criterion. Additionally, it is explained that the same approach can be utilized to obtain multivariate density estimates. The proposed orthogonal polynomial density estimation methodology is applied to several univariate and bivariate data sets.
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