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Abstract Details
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Activity Number:
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344
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #301839 |
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Title:
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A Linear Approximation Method for Parameter Estimation in Probit Regression
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Author(s):
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Haoyu Wang and Subir Ghosh*+
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Companies:
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University of California at Riverside
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Address:
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Department of Statistics, Riverside, CA, 92521,
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Keywords:
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Discrete Data ;
Linear Approximation ;
Maximum Likelihood ;
Probit Regression
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Abstract:
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In the Maximum Likelihood Estimation of the parameters in a probit regression model, we introduce a new approximate method to obtain the estimates. With our linear approximation, we find the exact solution of the Maximum Likelihood estimating equations. We compare our estimates with the standard numerical method estimates with a real data as well as a simulated data. We also present some theoretical properties of our estimates.
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