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The Quantitative Area Presents: Augustine Wong, Ph.D.

Augustine Wong
February 24, 2020
12:30PM - 1:30PM
Psychology Building, Room 35

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Add to Calendar 2020-02-24 12:30:00 2020-02-24 13:30:00 The Quantitative Area Presents: Augustine Wong, Ph.D. Title Talk: "A Simple and Accurate Likelihood-Based Inference Method" Description: Statistical inference is a process of evaluating how far away an estimate of a parameter obtained from an observed sample is from the true value of that parameter. In most cases, a confidence region of the parameter or a probability that measures the distance between the estimate and the true values is reported. Exact statistical inferential methods are available for some specific problems. However, they are not available for many realistic problems of interest, so asymptotic methods are needed in these cases. In this presentation, the standard likelihood-based asymptotic methods (Wald, Rao, and Wilks) are reviewed. Since these methods require large sample size, a simple improvement to the Wilks method is propsed with the aid of the bootstrap method. Simulation results show that the proposed procedure gives extremely accurate coverage even when the sample size is small. About Augustine Wong, Ph.D.: Dr. Augustine Wong is a Professor of Mathematics and Statistics at York University. His research focuses on computational methods in statistics, foundation of inferences, likelihood-based asymptotic inference, and statistical theory with applicaitons to econometrics, finance, and survival data analysis.   Psychology Building, Room 35 Department of Psychology ASC-psychmainoffice@osu.edu America/New_York public

Title Talk: "A Simple and Accurate Likelihood-Based Inference Method"

Description: Statistical inference is a process of evaluating how far away an estimate of a parameter obtained from an observed sample is from the true value of that parameter. In most cases, a confidence region of the parameter or a probability that measures the distance between the estimate and the true values is reported. Exact statistical inferential methods are available for some specific problems. However, they are not available for many realistic problems of interest, so asymptotic methods are needed in these cases. In this presentation, the standard likelihood-based asymptotic methods (Wald, Rao, and Wilks) are reviewed. Since these methods require large sample size, a simple improvement to the Wilks method is propsed with the aid of the bootstrap method. Simulation results show that the proposed procedure gives extremely accurate coverage even when the sample size is small.

About Augustine Wong, Ph.D.: Dr. Augustine Wong is a Professor of Mathematics and Statistics at York University. His research focuses on computational methods in statistics, foundation of inferences, likelihood-based asymptotic inference, and statistical theory with applicaitons to econometrics, finance, and survival data analysis.