Multivariate quantile contours are effective tools to summarize and
visualize the joint distributions. However, no easy solutions exist when dynamic and
conditional quantile contours are needed without strong distributional
assumptions. In this talk, we discuss a new modeling and estimation stradgey that
allows taking covariate effect into multivariate quantile functions. We will also discuss
the choice of models, their impact on the estimation, and a practical
guideline for model selection. In the end, we illustrate one of its applications in constructing
bivariate growth charts of young children
in the u.s.
3:00pm - 4:00pm
2203 SAS Hall
Refreshments will be served in the 2nd floor Hallway at 2:30pm.
NOTE: No food or drink is allowed in any of the classrooms in SAS Hall.