Events

Thursday, September 28, 2017 — 4:00 PM EDT
Susan Murphy

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time


A formidable challenge in designing sequential treatments is to  determine when and in which context it is best to deliver treatments.  Consider treatment for individuals struggling with chronic health conditions.  Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment.   That is, the treatment is adapted to the individual's context; the context may include  current health status, current level of social support and current level of adherence for example.  Data sets on individuals with records of time-varying context and treatment delivery can be used to inform the construction of the decision rules.    There is much interest in personalizing the decision rules, particularly in real time as the individual experiences sequences of treatment.   Here we discuss our work in designing  online "bandit" learning algorithms for use in personalizing mobile health interventions. 

Tuesday, September 26, 2017 — 4:00 PM EDT

Owning Your Research: Things I wish I knew for graduate study

Pursuing graduate study is a courageous decision for life that takes time, effort, and commitment. Graduate study can be mysterious at the beginning, miserable in the process, and marvelous at the end. As a recent grad, I am going to share some of my graduate study experiences in this presentation. In particular, I hope to give some advice to current graduate students to make their graduate study easier, faster, and happier. Among other recommendations, I will provide some tricks and tips on coding that can improve research productivity. 

Thursday, September 21, 2017 — 4:00 PM EDT

Empirical balancing scores and balancing weights


Propensity scores have been central to causal inference and are often used as balancing scores or balancing weights. Estimated propensity scores, however, may exhibit undesirable finite-sample performance. We take a step back to understand what properties of balancing scores and weights are desirable. For balancing scores, the dimension reduction aspect is important; whereas for balancing weights, a conditional moment balancing property is crucial. Based on these considerations, a joint sufficient dimension reduction framework is proposed for balancing scores, and a covariate functional balancing framework is proposed for balancing weights. 

Monday, September 18, 2017 — 4:00 PM EDT

Multivariate Quantiles: Nonparametric Estimation and Applications to Risk Management

In many applications of hydrology, quantiles provide important insights in the statistical problems considered. In this talk, we focus on the estimation of a notion of multivariate quantiles based on copulas and provide a nonparametric estimation procedure. These quantiles are based on particular level sets of copulas and admit the usual probabilistic interpretation that a p-quantile comprises a probability mass p. We also explore the usefulness of a smoothed bootstrap in the estimation process. Our simulation results show that the nonparametric estimation procedure yields excellent results in finite samples and that the smoothed bootstrap can be beneficially applied.

Thursday, September 14, 2017 — 4:00 PM EDT

Variable selection for case-cohort studies with failure time outcome

Case-cohort designs are widely used in large cohort studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large, so an efficient variable selection method is necessary. We investigated the properties of a variable selection procedure using the smoothly clipped absolute deviation penalty in a case-cohort design with a diverging number of parameters. We establish the consistency and asymptotic normality of the maximum penalized pseudo-partial-likelihood estimator, and show that the proposed variable selection method is consistent and has an asymptotic oracle property. Simulation studies compare the finite-sample performance of the procedure with tuning parameter selection methods based on the Akaike information criterion and the Bayesian information criterion. We make recommendations for use of the proposed procedures in case-cohort studies, and apply them to the Busselton Health Study.

Friday, September 8, 2017 — 10:00 AM EDT

Clustering Heavy-Tailed Stable Data

Tuesday, September 5, 2017 — 4:00 PM EDT

Why risk is so hard to measure?


This paper analyzes the reliability of standard approaches for financial risk analysis. We focus on the difference between Value-at-Risk and Expected Shortfall, their small sample properties, the scope for underreporting risk and how estimation can be improved. Overall, we find that risk forecasts are extremely uncertain at low sample sizes, with Value-at-Risk more accurate than Expected Shortfall. Value-at-Risk is easily deliberately underreported without violating regulations and control mechanisms. Finally, we discuss the implications for academic research, practitioners and regulators, along with best practice suggestions.

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