This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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127
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #307597 |
Title:
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Analysis of Constrained Generalized Linear Models with Missing Data
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Author(s):
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Karelyn Davis*+ and Sanjoy Sinha and Chul Gyu Park
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Companies:
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Carleton University/Health Canada and Carleton University and Carleton University
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Address:
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Health Canada, Ottawa, ON, K1A 0K9, Canada
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Keywords:
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Constrained inference ;
EM algorithm ;
Generalized linear model ;
Maximum likelihood
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Abstract:
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Statisticians often encounter data that is non-normally distributed, and includes some missing observations. For the former case, generalized linear models (GLM) are typically considered in the statistical analysis; whereas missing data are commonly analyzed through imputation or the Expectation-Maximization (EM) algorithm. While many procedures have been proposed for these models, only a few researchers have studied estimation and hypothesis testing in the presence of parameter constraints. The presentation will discuss maximum likelihood inference for such models under linear inequality constraints, using a gradient projection version of the EM algorithm. Simulation results and an application to detection of blood contaminants for an environmental study will also be discussed.
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Authors who are presenting talks have a * after their name.
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