Activity Number:
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319
- Highlights of the Canadian Journal of Statistics
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Type:
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Invited
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Date/Time:
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Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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Abstract #307970
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Presentation
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Title:
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Big Data and Partial Least-Squares Prediction
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Author(s):
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Dennis Cook* and Liliana Forzani
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Companies:
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University of Minnesota and Departamento de Matematica, Universidad Nacional del Litoral
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Keywords:
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Abstract:
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We give a brief commentary on the challenges of big data for Statistics. We then narrow our discussion to one of those challenges: dimension reduction. This leads to consideration of one particular dimension reduction method—partial least-squares (PLS) regression—for prediction in big high-dimensional regressions where the sample size and the number of predictors are both large. We show that in some regression contexts single-component PLS predictions converge at the usual root-n rate as (n, p) go to infinity regardless of the relationship between the sample size n and number of predictors p. Asymptotically, PLS predictions then behave as regression predictions in the usual context where p is fixed and n goes to infinity. These results support the conjecture that PLS regression can be an effective method for prediction in big high-dimensional regressions.
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Authors who are presenting talks have a * after their name.
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