This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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538
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract - #306669 |
Title:
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Minimum Quantile Distance Estimation of Quantiles Using Linear Combinations of Standard Quantile Functions as Models
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Author(s):
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Dean H. Fearn *+ and Elliott Nebenzahl
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Companies:
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California State University, East Bay and California State University, East Bay
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Address:
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PO Box 4294 , Stockton , CA, 95204-0294,
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Keywords:
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quantile estimation ;
minimum distance ;
linear model ;
maximum likelihood
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Abstract:
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We select the constant quantile function along with at least one other well known standard quantile function(s) and then select the coefficients corresponding to these functions that minimize the distance between the ensuing linear combination and the sample quantile function. This linear combination is thus assured to be close to the sample quantile function. We use this linear combination approach to make inferences (estimation and/or hypothesis testing and/or confidence intervals) about pth quantiles of the true distribution for small p. We compare our technique of estimating these quantiles to classical methods, such as maximum likelihood, and show that our technique is more robust, in that the technique introduced by us tends to be more accurate over a wider class of possible true distributions.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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