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Fall 2009

Wednesday December 2, 2009, 11am

Speaker: Beth Andrews, Assistant Professor of Statistics, Northwestern University

Title: Rank-Based Estimation for Time Series Model Parameters 
Abstract: The focus of this talk is rank-based estimation for time series model parameters. The parameter estimates considered minimize the sum of mean-corrected model residuals weighted by a function of residual rank, and are similar to the rank estimates proposed by L.A. Jaeckel [Estimating regression coefficients by minimizing the dispersion of the residuals, Ann. Math. Statist. 43 (1972) 1449–1458] for estimating linear regression parameters. Rank estimates are known to be robust and relatively efficient. It will be shown this is true in the case of parameter estimation for standard linear and nonlinear time series processes. The estimation technique is robust because the rank estimates are n^{1/2}-consistent (n represents sample size) and asymptotically normal under mild conditions. Since the weight function can be chosen so that rank estimation has the same asymptotic efficiency as maximum likelihood estimation, rank estimation is also relatively efficient. In addition, rank estimation dominates traditional Gaussian quasi-maximum likelihood estimation with respect to both robustness and asymptotic efficiency.

Wednesday November 18, 2009, 11am

Speaker: Jie Yang, Assistant Professor, Dept. of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago

Title: Classification Model Based on Permanent Process 
Abstract: This talk introduces a new statistical model based on a  permanent process for supervised classification problems. Unlike many research works in the literature, the permanent model assumes only exchangeability instead of independence on observations. Regardless of the number of classes or the dimension of the feature variables, the model may require only 2-3 parameters for fitting the covariance structure within clusters. It works well even if the class occupies non-convex, disjoint regions, or regions overlapped with other classes in the feature space. The application to DNA microarray analysis indicates that the permanent model is more capable of handling high-dimensional data. It can employ more feature variables in an efficient way and reduce the prediction error significantly. This is critical when the true classification relies on non-reducible high-dimensional features.

Wednesday November 4, 2009, 11am

Speaker: Donald Hedeker, Professor of Biostatistics, University of Illinois at Chicago

Title: Multilevel Models for Ecological Momentary Assessment (EMA) Data: An Application of a Mixed-Effects Location Scale Model 
Abstract: For longitudinal data, multilevel models include random subject effects to indicate how subjects influence their responses over the repeated assessments.  The error variance and the variance of the random effects are usually considered to be homogeneous.  These variance terms characterize the within-subjects (i.e., error variance) and between-subjects (i.e., random-effects variance) variation in the data.  In studies using Ecological Momentary Assessment (EMA), up to thirty or forty observations are often obtained for each subject, and interest frequently centers around changes in the variances, both within- and between-subjects.  In this presentation, focus is on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation.  In terms of the multilevel model, covariates are allowed to influence the mood variances to address this.  Also, a subject-level random effect is added to the within-subject variance specification.  This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses.  Additionally, the location and scale random effects are allowed to be correlated.  These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.

Wednesday October 21, 2009, 11am

Speaker: Dr. Liqun Xi, Northwestern University

Title: The minimum capture proportion for reliable estimation in capture-recapture models 
Abstract: For capture-recapture models, a reliable estimate for the population size is possible only for a reasonably large capture proportion, especially for a heterogeneous population. In all capture-recapture models, how large the capture proportion should be to ensure a reliable estimate for the population size is an important question. In this seminar, an idea for obtaining the minimum capture proportions for reliable estimation in capture-recapture models is introduced. Some results are presented, as well a real capture- recapture data with the application of the proposed results.

Wednesday October 7, 2009, 11am

Speaker: Yuan Liao, Northwestern University

Title: Posterior Consistency of Nonparametric Conditional Moment Restricted Models 
Abstract: This paper considers the nonparametric conditional moment restricted model that was previously studied by Ai and Chen (2003). We look at the estimation of the nonparametric structural function in a Bayesian way, starting by transforming the conditional restrictions equivalently into infinite number of unconditional moment restrictions, and then derive the posterior distribution of the parameter of interest based on the limited information likelihood. We focus on the frequentist properties of the posterior distribution, allowing the nonparametric structural function to be partially identified. It is shown that the posterior converges to any neighborhood of the identified region. Finally, we apply our results to the nonparametric instrumental regression model and the single index model.

Winter 2009

Friday, April 17, 2009 at 2 pm (Unusual TIME and LOCATION)

Joint Seminar with IEMS Department
Seminar Room:
 Tech, Room M228 (2145 Sheridan Road, Evanston)
Speaker: Cyrus R. Mehta, President of Cytel Corporation & Adjunct Professor of Biostatistics, Harvard University
Title: Design and Implementation of Late-Stage Adaptive Trials: Experiences of an Industry Consultant
Abstract: Sound statistical principles combined with careful planning of the logistical details are essential for successful implementation of an adaptive clinical trial. In this presentation I will share my experience as a consultant involved in several late stage adaptive designs. Topics that I will cover include sample size re-estimation, population enrichment and seamless phase II/III design. Each topic will be illustrated with a real case study. The crucial role of simulation will be highlighted. Regulatory experiences will be discussed.

Wednesday, May 6, 2009 at 11 am 

Speaker: Joseph Kang, Assistant Professor of Biostatistics, Department of Preventive Medicine, Northwestern University

Title: Causal inference for weight control behaviors among adolescent girls

Abstract: Overweight and obesity often begin in childhood, but few successful models exist for prevention and treatment of obesity in children and adolescent. Among adolescent girls, dieting may prospectively predict weight gain. Due to possible reciprocal causality between diet and weight gain, a quantitative causal analysis is necessary. During past two decades, Rubin Causal Model (RCM) has been known to idealize a successful design to quantify causal effects. In this talk, some inferential strategies using RCM will be discussed in order to estimate causal effects of weight control behaviors. The first session of this talk will be dedicated to a discussion of 1) recent semiparametric methods to infer causal estimands in RCM and 2) the importance of moderators that change the causal effects of dieting. The second session will discuss an extended RCM to adjust for the measurement error of weight control behaviors using latent class model. We use the National Longitudinal Study of Adolescent Health (Add health) data set for entire analyses.

Wednesday, May 13, 2009 at 11 am 

Speaker: Viktor Todorov, Assistant Professor, Department of Finance, Kellogg School of Management, Northwestern University

Title: Limit Theorems for Power Variations of Pure-Jump Processes with Application to Activity Estimation

Abstract: This paper derives the asymptotic behavior of realized power variation of pure-jump Ito semimartingales as the sampling frequency within a fixed interval increases to infinity. We prove convergence in probability and an associated central limit theorem for the realized power variation on the space of functions of the power equipped with a local uniform topology. We apply the limit theorems to propose an efficient adaptive estimator of the activity of discretely-sampled Ito semimartingale over a fixed interval.

Wednesday, May 27, 2009 at 11 am 

Speaker: Junhui Wang, Assistant Professor, Department of Mathematics, Statistics,
and Computer Science, UIC

Title: To be announced

Abstract: To be announced