Abstract Details
Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #307838 |
Title:
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Computing Confidence Intervals for Log-Concave Densities
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Author(s):
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Mahdis Azadbakhsh*+ and Hanna Jankowski and Xin Gao
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Companies:
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York University and York University and York University
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Keywords:
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non-parametric density estimation ;
log-concave ;
maximum likelihood ;
confidence interval
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Abstract:
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In recent years, shape-constraints have been introduced as an effective tool in non-parametric density estimation and the assumption of log-concavity has received particular attention. Balabdaoui et al.(2009) developed pointwise asymptotic theory for the maximum likelihood estimator of the log-concave density. Here, the practical aspects of their result are studied by calculating a pointwise confidence interval for the true log-concave density based on their asymptotic theory. To do this, two quantities need to be approximated: quantiles of the limiting process, and a nuisance parameter which involves the rst and second derivatives of the unknown log-concave density. The obtained confidence interval is studied via simulation.
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Authors who are presenting talks have a * after their name.
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