Abstract Details
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Activity Number:
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228
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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Abstract #312617
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View Presentation
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Title:
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M-Estimation Under Dependence
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Author(s):
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Pramita Bagchi*+ and Moulinath Banerjee and Stilian Stoev
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Companies:
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University of Michigan and University of Michigan and University of Michigan
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Keywords:
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M-Estimation ;
Dependence ;
Empirical Process ;
Mixing
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Abstract:
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We focus on developing the theoretical foundations of M-estimation for dependent data. M-estimation is a popular technique to extract a parameter estimate by minimizing a particular loss function. Previous work on the subject focused on specific models of dependence and addressed limited parametric scenario. Our goal is to contribute new theory to this important area that will provide unifying tools to address a range of applications. We have made important progress by proving a general triangular array version of a limit theorem for empirical processes under dependence. Applications of this result to classic cube-root asymptotics settings that were previously studied for independent data are being considered.
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Authors who are presenting talks have a * after their name.
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