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--- seminars:stat:160303 [2016/03/01 16:48] – shang
+++ seminars:stat:160303 [2016/03/01 19:21] (current) – shang
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =September 10, 2015~~
+<WRAP 70% center>
+^  **DATE:**|Thursday, March 3, 2016 |
+^  **TIME:**|1:15pm to 2:15pm |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Ganggang Xu, Binghamton University |
+^  **TITLE:**|Efficient Maximum Approximated Likelihood Inference for Tukey's g-and-h Distribution|
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Tukey's g-and-h distribution has been a powerful tool for data exploration and modeling since its introduction. However, two long standing challenges associated with this distribution family have remained unsolved until this day: how to find an optimal estimation procedure and how to make valid statistical inference on unknown parameters. To overcome these two challenges, a computationally efficient estimation procedure based on maximizing an approximated likelihood function of the Tukey's g-and-h distribution is proposed and is shown to have the same estimation efficiency as the maximum likelihood estimator under mild conditions. The asymptotic distribution of the proposed estimator is derived and a series of approximated likelihood ratio test statistics are developed to conduct hypothesis tests involving two shape parameters of Tukey's g-and-h distribution. Simulation examples and an analysis of air pollution data are used to demonstrate the effectiveness of the proposed estimation and testing procedures.
+</WRAP>