2012 Fall

Date: 2012-10-05 Time: 14:30-15:30 Location: BURN 1205 Abstract: Using only bivariate copulas as building blocks, regular vines(R-vines) constitute a flexible class of high-dimensional dependence models. In this talk we introduce a Markov switching R-vine copula model, combining the flexibility of general R-vine copulas with the possibility for dependence structures to change over time. Frequentist as well as Bayesian parameter estimation is discussed. Further, we apply the newly proposed model to examine the dependence of exchange rates as well as stock and stock index returns.

Date: 2012-09-28 Time: 14:30-15:30 Location: BURN 1205 Abstract: In this talk, I will provide an introduction to DTRs and an overview the state of the art (and science) of Q-learning, a popular tool in reinforcement learning. The use of Q-learning and its variance in randomized and non-randomized studies will be discussed, as well as issues concerning inference as the resulting estimators are not always regular. Current and future directions of interest will also be considered.

Date: 2012-09-21 Time: 14:30-15:30 Location: McGill, Burnside Hall 1214 Abstract: In many scientific experiments we need to face analysis with functional data, where the observations are sampled from random process, together with a potentially large number of non-functional covariates. The complex nature of functional data makes it difficult to directly apply existing methods to model selection and estimation. We propose and study a new class of penalized semiparametric functional linear regression to characterize the regression relation between a scalar response and multiple covariates, including both functional covariates and scalar covariates.