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Seminars 2014-2015     

Upcoming


Wednesday, January 28, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Arnon Boneh (Tel Aviv University)

Title: Review of the classical group-testing problem and some new results

Abstract: The classical (I,N,q) group-testing (GT) problem is : In a (finite) population of N identical members each one either has a well defined property (="good") or does not have it (="bad"). There are N corresponding i.i.d. random variables each having probability q of being "good"(07 ​and​ ​t​he best known algorithm for this problem is superexponential in N. Little is known about the optimal group-testing policy​ although​ there are several algorithms that provide fairly good sub-optimal solution for large N values and almost any q value​.​ Some of these algorithms will be mentioned in the seminar. Special emphasi​s​ will be given to a new "halving" algorithm which is simple to execute on one hand and is bounded away from the optimal solution by no more than 0,4% in the hard core of the problem.​



Past


Wednesday, November 19, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Joseph Barr (Chief Analytics Officer, HomeUnion, Irvine, CA)

Title: Real Estate Analytics

Abstract:Real estate plays a significant part of our economy and there's no wonder that when home prices bottom out, so does the economy. The talk is about the Analytics of real estate, how location determines value, demographic dynamics, households, measuring and analyzing trends.

 


Wednesday, November 12, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Damla Senturk (UCLA)

Title: Generalized Multi-Index Varying Coefficient ModelsGeneralized Multi-Index Varying Coecient Models

Abstract:Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the U.S. Finally, simulation studies assess the performance of the proposed estimation procedures.

 


Wednesday, November 5, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Wenguang Sun (USC)

Title: False Discovery Control in Large-Scale Spatial Multiple Testing

Abstract: This talk considers both point-wise and cluster-wise spatial multiple testing problems. We derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets.

Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US. This is the joint work with Brian Reich, Tony Cai, Michele Guindani and Armin Schwartzman.


 


Wednesday, October 22, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Sreenivas Konda (UCSB)

Title: Consistency of Large Autocovariance Matrices

Abstract:We consider Autoregressive (AR) processes of large p, but less than n, to approximate a linear time series. Using Bartlett's formula and strong mixing conditions, we show the consistency of the large sample autocovariance matrix by banding procedure. These large sample autocovariance matrices are consistent in operator norm as long as (log p)/n goes to 0. Parameters of large AR(p) model are estimated using a regularization procedure and banding of the autocovariance matrix. We also briefly review application of banding in finding the inverse of sum of two special matrices. Real examples from physics and business  are used to illustrate the proposed methods.

 


Wednesday, October 8, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Ania Supady-Chavan (KeyCorp)

Title: Time Series Modeling and Forecasting an application to Banks’ stress-testing process.

Abstract: I want to invite you to participate in a small presentation on how time series modeling can be performed to establish position during simulated stress. My goal is to gain your interest in the area of challenging current modeling techniques and looking beyond standard model assumptions testing to assess the true risk of the formulated model for the intended use. I am interested in exploring the procedures that happen behind the scenes of any code’s syntax to better explore statistics that play crucial role in assessing models performance as well as the forecasting process. The forecasting of next periods ahead is the process that I would like to emphasize the most.


Wednesday, December 3, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Julie Swenson (UCSB)

Title: A Bayesian Approach to Recommendation Systems.

Abstract: Recommendation systems have proliferated in the last decade. Currently, most recommendation systems utilize content based algorithms, collaborative filtering based algorithms, or a combination of both. The recent surge in popularity of social networks has led to the creation of trust based algorithms. While such recommendation systems have proven that they can be more successful than their content and collaborative filtering based counterparts, they are often are plagued with problems with cold start and data sparseness. We propose a Bayesian trust based algorithm that addresses both of these problems. Our results indicate that our method can be more successful than an existing Bayesian trust based algorithm.


Monday, January 5, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Jason Lee(Stanford University)

Title: Selective Inference via the Condition on Selection Framework: Applications to Inference After Variable Selection

Abstract: Selective Inference is the problem of testing hypotheses that are chosen or suggested by the data. Inference after variable selection in high-dimensional linear regression is a common example of selective inference; we only estimate and perform inference for the selected variables. We propose the Condition on Selection framework, which is a framework for selective inference that allows selecting and testing hypotheses on the same dataset. In the case of inference after variable selection (variable selection by lasso, marginal screening, or forward stepwise), the Condition on Selection framework allows us to construct confidence intervals for regression coefficients, and perform goodness-of-fit testing for the selected model. This is done by deriving the distribution of the test statistic conditioned on the selection event.


Wednesday, January 7, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Erin Schliep (Duke University)

Title: Stochastic Modeling for Environmental Velocities

Abstract:  The velocity of climate change is defined as an instantaneous rate of change needed to maintain a constant climate. It is computed as the ratio of the temporal gradient of climate change over the spatial gradient of climate change. Ecologically, understanding these rates of climate change is critical since the range limits of plants and animals are changing in response to climate change. A fully stochastic hierarchical model is proposed that incorporates the inherent relationship between climate, time, and space. Space-time processes are employed to capture the spatial correlation in both the climate variable and the rate of change in climate over time. Directional derivative processes yield spatial and temporal gradients and, thus, the resulting velocities for a climate variable. The gradients and velocities can be obtained at any location in any direction and any time. Maximum gradients and their directions can also be obtained and, as a result, minimum velocities. Explicit parametric forms for the directional derivative processes provide full inference on the gradients and velocities including estimates of uncertainty. The model is applied to average annual temperature across the eastern United States for the years 1963 to 2012. Maps of the spatial and temporal gradients are produced as well as velocities of temperature change. This work provides a framework for future research in stochastic modeling of other environmental velocities, such as the velocity of disease epidemics or species distributions across a region.



Friday, January 9, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Luo Xiao (Johns Hopkins University)

Title: Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional data approach

Abstract: Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging (BLSA) physical activity was recorded every minute for 773 adults for an average of 4.5 days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern we introduce a practical bivariate smoother and two crucial innovations: 1) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation; and 2) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.


Monday, January 12, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Yuekai Sun (Stanford University)

Title: Distributed estimation and inference for sparse regression

Abstract: We address two outstanding challenges in sparse regression: (i) computationally efficient estimation in distributed settings (ii) valid inference for the selected coefficients. The main computational challenge in a distributed setting is harnessing the computational capabilities of all the machines while keeping communication costs low. We devise an approach that requires only a single round of communication among the machines. We show the approach recovers the convergence rate of the (centralized) lasso as long as each machine has access to an adequate number of samples. Turning to the second challenge, we devise an approach to post-selection inference by conditioning on the selected model. In a nutshell, our approach gives inferences with the same frequency interpretation as those given by data/sample splitting, but it is more broadly applicable and more powerful. The validity of our approach also does not depend on the correctness of the selected model; i.e. it gives valid inferences even when the selected model is incorrect.


Wednesday, January 14, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Johann Gagnon-Bartsch (University of California Berkeley)

Title: Removing Unwanted Variation with Negative Controls

Abstract: High-throughput biological data, such as microarray data and gene sequencing data, are plagued by unwanted variation -- systematic errors introduced by variations in experimental conditions such as temperature, the chemical reagents used, etc. This unwanted variation is often stronger than the biological variation of interest, making analysis of the data challenging, and severely impeding the ability of researchers to capitalize on the promise of the technology. One of the biggest challenges to removing unwanted variation is that the factors causing this variation (temperature, atmospheric ozone, etc.) are unmeasured or simply unknown. This makes the unwanted variation difficult to identify; the problem is essentially one of unobserved confounders. In my talk, I will discuss the use of negative controls to help solve this problem. A negative control is a variable known a priori to be unassociated with the biological factor of interest. I will begin with an example that will introduce the notion of negative controls, and demonstrate the effectiveness of negative controls in dealing with unwanted variation. I will then discuss negative controls more generally, including a comparison with instrumental variables.


Friday, January 16, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Heng Yang (City University of New York)

Title: Simultaneous Detection And Identification With Post-Change Uncertainty

Abstract: We consider the problem of quickest detection of an abrupt change when there is uncertainty about the post-change distribution. Because of the uncertainty, We would like not only detecting the change point but also identifying the post-change distribution simultaneously. In particular we examine this problem in the continuous-time Wiener model where the drift of observations changes from zero to a drift randomly chosen from a collection. We set up the problem as a stochastic optimization in which the objective is to minimize a measure of detection delay subject to a frequency of false alarm constraint, while also identifying the value of the post-change drift up to pre-specified error bounds. We consider a composite rule involving the CUSUM reaction period, that is coupled with an identification function, and show that by choosing parameters appropriately, such a pair of composite rule and identification function can be asymptotically optimal of first order to detect the change point and simultaneously satisfies the error bounds to identify the post-change drift as the average first false alarm increases without bound. We also discuss the detection problem along under some situations.



Friday, January 9, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Luo Xiao (Johns Hopkins University)

Title: Quantifying the lifetime circadian rhythm of physical activity: a covariate-dependent functional data approach

Abstract: Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging (BLSA) physical activity was recorded every minute for 773 adults for an average of 4.5 days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern we introduce a practical bivariate smoother and two crucial innovations: 1) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation; and 2) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.


Monday, January 12, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Yuekai Sun (Stanford University)

Title: Distributed estimation and inference for sparse regression

Abstract: We address two outstanding challenges in sparse regression: (i) computationally efficient estimation in distributed settings (ii) valid inference for the selected coefficients. The main computational challenge in a distributed setting is harnessing the computational capabilities of all the machines while keeping communication costs low. We devise an approach that requires only a single round of communication among the machines. We show the approach recovers the convergence rate of the (centralized) lasso as long as each machine has access to an adequate number of samples. Turning to the second challenge, we devise an approach to post-selection inference by conditioning on the selected model. In a nutshell, our approach gives inferences with the same frequency interpretation as those given by data/sample splitting, but it is more broadly applicable and more powerful. The validity of our approach also does not depend on the correctness of the selected model; i.e. it gives valid inferences even when the selected model is incorrect.


Wednesday, January 14, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Johann Gagnon-Bartsch (University of California Berkeley)

Title: Removing Unwanted Variation with Negative Controls

Abstract: High-throughput biological data, such as microarray data and gene sequencing data, are plagued by unwanted variation -- systematic errors introduced by variations in experimental conditions such as temperature, the chemical reagents used, etc. This unwanted variation is often stronger than the biological variation of interest, making analysis of the data challenging, and severely impeding the ability of researchers to capitalize on the promise of the technology. One of the biggest challenges to removing unwanted variation is that the factors causing this variation (temperature, atmospheric ozone, etc.) are unmeasured or simply unknown. This makes the unwanted variation difficult to identify; the problem is essentially one of unobserved confounders. In my talk, I will discuss the use of negative controls to help solve this problem. A negative control is a variable known a priori to be unassociated with the biological factor of interest. I will begin with an example that will introduce the notion of negative controls, and demonstrate the effectiveness of negative controls in dealing with unwanted variation. I will then discuss negative controls more generally, including a comparison with instrumental variables.


Friday, January 16, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Heng Yang (City University of New York)

Title: Simultaneous Detection And Identification With Post-Change Uncertainty

Abstract: We consider the problem of quickest detection of an abrupt change when there is uncertainty about the post-change distribution. Because of the uncertainty, We would like not only detecting the change point but also identifying the post-change distribution simultaneously. In particular we examine this problem in the continuous-time Wiener model where the drift of observations changes from zero to a drift randomly chosen from a collection. We set up the problem as a stochastic optimization in which the objective is to minimize a measure of detection delay subject to a frequency of false alarm constraint, while also identifying the value of the post-change drift up to pre-specified error bounds. We consider a composite rule involving the CUSUM reaction period, that is coupled with an identification function, and show that by choosing parameters appropriately, such a pair of composite rule and identification function can be asymptotically optimal of first order to detect the change point and simultaneously satisfies the error bounds to identify the post-change drift as the average first false alarm increases without bound. We also discuss the detection problem along under some situations.


Wednesday, January 21, South Hall 5607F, 3:30-5PM, Refreshments served at 3:15 PM

Dr. Jennifer Bobb (Harvard University)

Title: Beyond the one-exposure, one-outcome paradigm for scientific discovery in environmental epidemiology

Abstract: The most common approach in environmental epidemiology is to hypothesize a relationship between a particular exposure and a particular outcome and then estimate the health risks. In this talk I will present two case studies from my research that move beyond this standard one-exposure, one-outcome paradigm. The first case study considers the problem of estimating the effects of multiple exposures on a single outcome. We propose a new approach for estimating the health effects of multi-pollutant mixtures, Bayesian kernel machine regression, which simultaneously estimates the (potentially high-dimensional) exposure-response function and incorporates variable selection to identify important mixture components. The second case study considers the effects of a single exposure (heat waves) on multiple outcomes (cause-specific hospitalization rates). Rather than pre-specifying a small number of individual diseases, we jointly consider all 15,000 possible discharge diagnosis codes and identify the full spectrum of diseases associated with exposure to heat waves among 23.7 million older adults. Through these case studies, we find that approaches that consider multiple exposures and/or multiple outcomes have the potential to lead to new scientific insights.

 


 

 

 
 
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