Abstract Details
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Activity Number:
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106
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Type:
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Invited
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307438 |
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Title:
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Multiple Linear Regression with Latent Factors
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Author(s):
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Patrick O. Perry*+ and Natesh S. Pillai and Paul Bourgade
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Companies:
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NYU Stern and Harvard University and Harvard University
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Keywords:
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multiple linear regression ;
principal components analysis ;
degrees of freedom ;
random matrix theory ;
latent factor model
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Abstract:
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We study multiple linear regression under the assumption that some of the covariates are unobserved. With multiple responses, these latent covariates can be estimated by applying principal components analysis to the matrix of regression residuals. A priori, it is not clear if this approach is valid. Using recent results from random matrix theory, we derive an asymptotically correct degrees of freedom estimate for this setting which allows adjusting for unobserved covariates in multiple linear regression models.
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Authors who are presenting talks have a * after their name.
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