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Activity Number:
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120
- SPEED: Nonparametric Statistics: Estimation, Testing, and Modeling
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #329523
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Presentation
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Title:
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Estimation of Regression Function Using Shannon's Entropy
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Author(s):
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Yi Mao* and Aman Ullah
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Companies:
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University of California, Riverside and University of California, Riverside
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Keywords:
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Shannon's entropy;
maximum entropy distributions;
regression function;
response function
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Abstract:
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We introduce an information theoretic approach-Shannon's entropy to specify econometric functions as an alternative to avoid parametric assumptions. We investigate the performances of Shannon's entropy in estimating the regression (conditional mean) and response (derivative) functions. We have demonstrated that they are easy to implement, and are advantageous over parametric models and nonparametric kernel techniques.
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Authors who are presenting talks have a * after their name.
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