Not many universities provide you with an opportunity to be involved in research at the undergraduate level. At UFV, not only do we encourage our faculty to incorporate research into their teaching, but we also support our students to conduct research and share their findings.
Engagement in research early in your academic career can help you obtain scholarships, awards, and graduate school positions. This is part of UFV's commitment to providing you with the best undergraduate education in Canada.
For other research opportunities in Science at UFV outside of Math and Stats, visit UFV Faculty of Science's Research page.
Analysis of the rare and extreme values through statistical modeling is an important issue in economic crises, climate forecasting, and risk management of financial portfolios. Extreme value theory provides the probability models needed for statistical modeling of extreme values. There are generally two ways to identifying the extreme values in a data set, the block-maxima and the peak-over-threshold method. The block-maxima method uses the Generalized Extreme Value distribution and the peak-over-threshold method uses the Generalized Pareto distribution. It is common that the location of these distributions kept fixed. It is possible that some unobserved variables produce heterogeneity in the location of the assumed distribution and change the percentiles of the distribution, an important criterion in economic crises and climate forecasting. In this project, we investigate on the importance of modeling the unobservable heterogeneity through random effects modeling.
It is common that the baseline is considered as an explanatory variable to describe the counts measured over time in longitudinal studies. There are many studies in clinical trials that assume the baseline as nonstochastic. We think that the same process that produced the longitudinal counts produces the baseline and so considering it as stochastic may improve the statistical model for identifying the significant factors such as treatment in clinical trials. In this research, we consider the baseline as stochastic and investigate on introducing an informative prior distribution for the baseline in a Bayesian framework.