Events - October 2018

Tuesday, October 30, 2018 — 4:00 PM EDT

Systemic risk and the optimal capital requirements in a model of financial networks and fire sales


I consider an interbank network with fire sales externalities and multiple illiquid assets and study the problem of optimally trading off between capital reserves and systemic risk. I find that the problem of measuring systemic risk and the optimal capital requirements under various liquidation rules can be formulated as a convex and convex mixed integer programming. To solve the convex MIP, I offer an iterative algorithm that converges to the optimal solutions. I show the results of the methodology through numerical examples and provide implications for regulatory policies and related research topics.

Thursday, October 25, 2018 — 4:00 PM EDT

Causal Inference with Unmeasured Confounding: an Instrumental Variable Approach

Causal inference is a challenging problem because causation cannot be established from observational data alone. Researchers typically rely on additional sources of information to infer causation from association. Such information may come from powerful designs such as randomization, or background knowledge such as information on all confounders. However, perfect designs or background knowledge required for establishing causality may not always be available in practice. In this talk, I use novel causal identification results to show that the instrumental variable approach can be used to combine the power of design and background knowledge to draw causal conclusions. I also introduce novel estimation tools to construct estimators that are robust, efficient and enjoy good finite sample properties. These methods will be discussed in the context of a randomized encouragement design for a flu vaccine.

Friday, October 19, 2018 — 11:00 AM EDT

Efficient Estimation, Robust Testing and Design Optimality for Two-Phase Studies


Two-phase designs are cost-effective sampling strategies when some covariates are expensive to be measured on all study subjects. Well-known examples include case-control, case-cohort, nested case-control and extreme tail sampling designs. In this talk, I will discuss three important aspects in two-phase studies: estimation, hypothesis testing and design optimality. First, I will discuss efficient estimation methods we have developed for two-phase studies. We allow expensive covariates to be correlated with inexpensive covariates collected in the first phase. Our proposed estimation is based on maximization of a modified nonparametric likelihood function through a generalization of the expectation-maximization algorithm. The resulting estimators are shown to be consistent, asymptotically normal and asymptotically efficient with easily estimated variances. Second, I will focus on hypothesis testing in two-phase studies. We propose a robust test procedure based on imputation. The proposed procedure guarantees preservation of type I error, allows high-dimensional inexpensive covariates, and yields higher power than alternative imputation approaches. Finally, I will present some recent development on design optimality. We show that for general outcomes, the most efficient design is an extreme-tail sampling design based on certain residuals. This conclusion also explains the high efficiency of extreme tail sampling for continuous outcomes and balanced case-control design for binary outcomes. Throughout the talk, I will present numerical evidences from simulation studies and illustrate our methods using different applications.

Wednesday, October 17, 2018 — 4:00 PM EDT
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Uncovering the Mechanisms of General Anesthesia: Where Neuroscience Meets Statistics


General anesthesia is a drug-induced, reversible condition involving unconsciousness, amnesia (loss of memory), analgesia (loss of pain sensation), akinesia (immobility), and hemodynamic stability. I will describe a primary mechanism through which anesthetics create these altered states of arousal. Our studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness​, in the case of propofol, and we have demonstrated ​​ that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. The success of our research has depended critically on tight coupling of experiments, ​statistical signal processing​​ and mathematical modeling.

Thursday, October 11, 2018 — 4:00 PM EDT

Probabilistic approaches to mine association rules


Mining association rules is an important and widely applied data mining technique for discovering patterns in large datasets. However, the used support-confidence framework has some often overlooked weaknesses. This talk introduces a simple stochastic model and shows how it can be used in association rule mining. We apply the model to simulate data for analyzing the behavior and shortcomings of confidence and other measures of interestingness (e.g., lift). Based on these findings, we develop a new model-driven approach to mine rules based on the notion of NB-frequent itemsets, and we define a measure of interestingness which controls for spurious rules and has a strong foundation in statistical testing theory.

Thursday, October 4, 2018 — 4:00 PM EDT

Methods for High Dimensional Compositional Data Analysis in Microbiome Studies


Human microbiome studies using high throughput DNA sequencing generate  compositional data with the absolute abundances of microbes not recoverable from sequence data alone. In compositional data analysis, each sample consists of proportions of various organisms with a unit sum constraint. This simple feature can lead traditional statistical methods when naively applied to produce errant results and spurious associations. In addition, microbiome sequence data sets are typically high dimensional, with the number of taxa much greater than the number of samples. These important features require further development of methods for  analysis of high dimensional compositional data.  This talk presents several latest developments in this area, including methods for estimating the compositions based on sparse count data,  two-sample test for compositional vectors and  regression analysis with compositional covariates.  Several microbiome studies at the University of Pennsylvania are used to illustrate these methods and several open questions will be discussed.

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