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Data Science Seminar Series – TRU Newsroom

Data Science Seminar Series – TRU Newsroom

February 3, 2021 at 2:17 pm  Education, Kamloops, News

Join TRU’s Faculty of Science for the Data Science Seminar Series.

This Wednesday Feb. 3 at 5:30 p.m. join TRU Faculty of Science Member Becky Lin as she presents Accurate inference for the mean of the Poisson-Exponential distribution.

Abstract:
Although the random sum distribution is a well-studied probability theory, inference for the mean of such distribution is very limited. In this talk, I will introduce two approaches to obtain inference for the mean of the Poisson-Exponential distribution. Both approaches require the log-likelihood function of the Poisson-Exponential distribution, which is not available. An approximate form of the log-likelihood function is then derived by the saddlepoint method. Inference about the mean of the Poisson-Exponential distribution can either be obtained from the modified signed likelihood root statistic or from the Bartlett corrected likelihood ratio statistic. The explicit form of the modified signed likelihood root statistic is derived in this paper, and a numerical method to approximate the Bartlett correction factor is also proposed. Simulation studies show that both methods are extremely accurate even when the sample size is small.

Click here to join event through BlueJeans.

Biography:
Wei (Becky) Lin is an assistant professor in TRU’s Department of Mathematics and Statistics. Lin obtained her B.Eng. in computer science in China and received her PhD in statistics in Canada under the supervision of Nancy Reid and D.A.S. Fraser at the University of Toronto (U of T). Before she joined TRU, Lin worked at U of T for one year. Additionally, she conducted nine months of post-doctoral research at the University of Southern California, co-founded an AI startup and worked on it for two years. Lin has broad research interests in likelihood inference, computational statistics, data visualization, statistical machine learning and neural network modelling for big data with a focus on acoustic data.

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