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Abstract Details
Activity Number:
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415
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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SSC
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Abstract - #306021 |
Title:
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Asymptotics of the Discrete Log-Concave Maximum Likelihood Estimator and Related Applications
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Author(s):
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Hanna Jankowski*+
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Companies:
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York University
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Address:
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Department of Math & Stats, Toronto, ON, M3J 1P3, Canada
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Keywords:
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shape-constraints ;
nonparamateric estimation ;
log-concave ;
confidence interval ;
misspecification
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Abstract:
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The assumption of log-concavity is a flexible and appealing nonparametric shape constraint in distribution modeling. In this work, we study the log-concave maximum likelihood estimator (MLE) of a probability mass function (pmf). We show that the MLE is strongly consistent and derive its pointwise asymptotic theory under both the well- and misspecified settings. Our asymptotic results are used to calculate confidence intervals for the true log-concave pmf. The MLE and associated condence bands may be easily computed using the R package logcondiscr. We illustrate our theoretical results using recent data from the H1N1 pandemic in Ontario, Canada.
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Authors who are presenting talks have a * after their name.
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