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Activity Number:
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35
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #305522 |
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Title:
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Multinomial Mixture Model with Heterogeneous Classification Probabilities
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Author(s):
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Mark D. Holland*+ and Brian R. Gray
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Companies:
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The University of Minnesota and U.S. Geological Survey
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Address:
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313 Ford Hall, Minneapolis, MN, 55455,
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Keywords:
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abundance estimation ; multinomial
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Abstract:
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Royle and Link (2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework and using Markov chain Monte Carlo. Based on simulations, this elaborated Royle-Link model yields effectively unbiased estimates of multinomial and correct classification probability estimates. The method is illustrated using categorical submersed aquatic vegetation data.
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