STATISTICS SEMINAR

SPEAKER:

Jiahua Chen

Department of Statistics and Actuarial Science

University of Waterloo

TITLE:

Empirical Likelihood Confidence Intervals for a Population Containing Many Zeros

ABSTRACT:

If a population contains many zero values and the sample size is not very large, the central limit theorem based confidence intervals for the population mean may have poor coverage probabilities. This problem is substantially reduced by constructing parametric likelihood ratio intervals when an appropriate mixture model can be found. However, in the context of survey sampling, a general preference is to make minimal assumption about the population. We have therefore investigated the coverage properties of nonparametric empirical likelihood confidence intervals for the population mean. Under a variety of hypothetical populations, the empirical likelihood intervals often outperformed parametric likelihood intervals by having larger lower bounds, or more balanced coverage rates. We have also used a real data set to illustrate the empirical likelihood method.

This is joint work with Shun-Yi Chen of Tamkang University and J. N. K. Rao of Carleton University.

About the Speaker

Jiahua Chen is a professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research interests are in diverse areas such as experimental design, statistical genetics, empirical likelihood, asymptotic theory, sampling from finite populations and finite mixture models. Professor Chen is originally from China receiving a B.Sc. from the University of Science and Technology of China and an M.Sc. from Institute of Systems Science, Academia Sinica. In 1990 he recieved his Ph.D. form the University of Wisconsin at Madison where he worked with Professor Jeff Wu.

Transparencies

You can download a pdf version of the transparencies and some tables of simulation results.

Department of Mathematics and Statistics

Graduate Program in Statistics

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Last updated on October 24, 2002