Activity Number:
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335
- SPEED: Reliable Statistical Learning and Data Science
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #324763
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Title:
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High-Dimensional Discriminant Analysis for Spatially Correlated Data
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Author(s):
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Yingjie Li* and Tapabrata Maiti
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Companies:
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Michigan State University and Michigan State University
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Keywords:
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Abstract:
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Linear discriminant analysis(LDA) is one of the most classical and popular classification techniques. It is still widely used in modern statistical applications. We investigate high dimensional discriminant analysis for spatially correlated data in this paper. The estimation of covariance structure is important in discriminant analysis, especially for spatially correlated data. Penalized maximum likelihood estimation (PMLE) is developed for feature selection and parameter estimation. Tapering technique is applied to reduce computation load. Extensive simulation study shows a significant improvement in classification performance under spatial dependence.
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Authors who are presenting talks have a * after their name.