### Empirical likelihood and Extremes

- Series
- Dissertation Defense
- Time
- Wednesday, November 16, 2011 - 15:00 for 1.5 hours (actually 80 minutes)
- Location
- Skiles 171
- Speaker
- Yun Gong – School of Mathematics, Georgia Tech

**Please Note:** Advisor: Liang Peng

In 1988, Owen introduced empirical likelihood as a nonparametric
method for constructing confidence intervals and regions.
It is well known that empirical likelihood has several attractive advantages
comparing to its competitors such as bootstrap: determining the
shape of confidence regions automatically; straightforwardly incorporating
side information expressed through constraints; being Bartlett correctable.
In this talk, I will discuss some extensions of the empirical likelihood
method to several interesting and important statistical inference situations
including: the smoothed jackknife empirical likelihood method for the
receiver operating characteristic (ROC) curve, the smoothed empirical
likelihood method for the conditional Value-at-Risk with the volatility
model being an ARCH/GARCH model and a nonparametric regression respectively. Then, I will
propose a method for testing nested stochastic models with discrete and
dependent observations.