2012 Fall

• • Oct 5, 2012
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  Markov switching regular vine copulas

  Jacob Stöber · Oct 5, 2012

  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. We show that changes in dependence are usually closely interrelated with periods of market stress. In such times the Value at Risk of an asset portfolio is significantly underestimated when changes in the dependence structure are ignored.

• • Sep 28, 2012
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  The current state of Q-learning for personalized medicine

  Erica Moodie · Sep 28, 2012

  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.

• • Sep 21, 2012
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  Regularized semiparametric functional linear regression

  Fang Yao · Sep 21, 2012

  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. The resulting method provides a unified and flexible framework to jointly model functional and non-functional predictors, identify important covariates, and improve efficiency and interpretability of the estimates. Featured with two types of regularization: the shrinkage on the effects of scalar covariates and the truncation on principal components of the functional predictor, the new approach is flexible and effective in dimension reduction. One key contribution of this paper is to study theoretical properties of the regularized semiparametric functional linear model. We establish oracle and consistency properties under mild conditions by allowing possibly diverging number of scalar covariates and simultaneously taking the infinite-dimensional functional predictor into account. We illustrate the new estimator with extensive simulation studies, and then apply it to an image data analysis.