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Activity Number: 541
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #305718
Title: Inference for Skew-Normal Data Using Stochastic Hellinger Distance Method and Stochastic MLE
Author(s): Serena Chan*+ and Anand Vidyashankar
Companies: Cornell University and Cornell University
Address: 202 Williams Street, Ithaca, NY, 14850,
Keywords: skew normal ; confidence coverage ; minimum hellinger distance estimators ; stochastic search
Abstract:

Let (xn, n>=1) be a random sample from a skew-normal population with parameters lambda1, lambda2, and lambda. The parameter lambda is related to the skewness parameter and called the index of skewness. We are developing two new methodologies, stochastic minimum Hellinger distance estimation (MHDE) and stochastic MLE (SMLE), to perform inferences for (lambda1, lambda2, lambda). We study the coverage properties and establish the asymptotic validity of the proposed methodologies. We also compare our proposed methodologies with the conventional MLE and semiparametric estimation method. We find that MHDE and SMLE perform well for modestly large values of the skewness parameter. Extensions to random effects models and other models related to longitudinal data are under investigation.

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