Donglin Zeng

University of North Carolina at Chapel Hill

Outcome Weighted Learning for Dynamic Treatment Regimes

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. Discovering DTRs from a SMART trial is challenging due to high-dimensional information and complex interactions between a patient's temporal characteristics and treatments. In this work, we introduce a new statistical learning method, namely outcome weighted learning (O-learning), for estimating the optimal DTR.

Thursday, October 24, 2013 - 3:30pm

Guanqun Cao

Auburn University

Spline Confidence Envelopes for Covariance Functions In Dense Functional/Longitudinal Data

We consider nonparametric estimation of the covariance function for dense functional data using computationally efficient tensor product B-splines. We develop both local and global asymptotic distributions for the proposed estimator, and show that our estimator is as efficient as an "oracle" estimator where the true mean function is known. Simultaneous confidence envelopes are developed based on asymptotic theory to quantify the variability in the covariance estimator and to make global inferences on the true covariance.

Thursday, October 31, 2013 - 3:30pm

Chunming Zhang

University of Wisconsin

Multiple Testing Via FDR_L for Large-Scale Imaging Data

The multiple testing procedure plays an important role in detecting the presence of spatial signals for large-scale imaging data. Typically, the spatial signals are sparse but clustered.

Thursday, November 14, 2013 - 3:30pm

Runze Li

Penn State

Feature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates

This paper is concerned with feature screening and variable selection for varying coefficient models with ultrahigh dimensional covariates. We propose a new feature screening procedure for these models based on conditional correlation coefficient. We systematically study the theoretical properties of the proposed procedure, and establish their sure screening property and the ranking consistency. To enhance the finite sample performance of the proposed procedure, we further develop an iterative feature screening procedure.

Thursday, November 21, 2013 - 3:30pm

Peihua Qiu

University of Florida

Statistical Process Screening System: An Approach For Identifying Irregular Longitudinal Patterns

In our daily life, we often need to identify individuals whose longitudinal patterns are different from the patterns of those well-functioning individuals, so that some unpleasant consequences can be avoided. In many such applications, observations of a given individual are obtained sequentially, and it is desirable to have a screening system to give a signal as soon as possible after that individual's longitudinal pattern starts to deviate from the regular pattern so that some adjustments or interventions can be made in a timely manner.

Thursday, March 20, 2014 - 3:30pm

Andy Bartlett

PhD Candidate, Statistics

Extreme Value Estimators for Various Non-Negative Time Series With Heavy-Tail Innovations

Extreme value theory is a branch of statistics that is devoted to studying the phenomena governed by extremely rare events. The modeling and statistics of such phenomena are tail dependent and so we consider a class of heavy-tail distributions, which are characterized by regular variation in the tails. While many articles have considered regular variation at one endpoint (particularly the left endpoint), the idea of regular variation at both endpoints has not be addressed. In this dissertation, we propose extreme value estimators for various non-negative time

Major Professor(s): 
William P. McCormick
Thursday, April 11, 2013 - 3:30pm

Xinyu Liu

PhD Candidate, Statistics

Sample size determination in multi-class classification and prediction based on single-nucleotide polymorphisms

Single-nucleotide polymorphisms (SNPs), believed to determine human differences, are widely used to predict risk of diseases and class membership of subjects. In the literature, several supervised machine learning methods, such as, support vector machine, neural network and logistic regression, are available for classification. Typically, however, samples for training a machine are limited and/or the sampling cost is high. Thus, it is essential to determine the minimum sample size needed to construct a classifier based on SNP data.

Major Professor(s): 
T.N. Sriram
Friday, March 22, 2013 - 12:30pm
Room 136, Poultry Science Building

Zaid Abdo


The ABCs of Experimental Evolution

Evolution is a complex process that involves many sources of variation and interactions that make mathematical modeling a challenge. In nature, evolution is a time dependent process that involves a large number of environmental variables influencing the adaptation of a population and its progress. Environmental effects include the interaction between a population with its physical environment, its interaction with other populations and species, and the within population interactions between its members.

Thursday, March 21, 2013 - 3:30pm

Nat Kulvanich

PhD Candidate, Statistics

Mixture Poisson Point Process: Assessing Heterogeneity in EMA Analysis

The times of repeated behavioral events can be viewed as a realization of a temporal point process. Rathbun, Shiffman, and Gwaltney (2007) used a Poisson process (Cox 1972) for modelling repeated behavioral events impacted by time-varying covariates. Taking an inspiration from the techniques of Generalized Linear Mixed Models, and the EM algorithm (Dempster et al. 1977) for finite mixture model estimation, we will further extend their models to handel data arising from a heterogeneous population.

Major Professor(s): 
Stephen L. Rathbun
Wednesday, March 6, 2013 - 3:00pm
201, BS Miller Hall, Health Sciences Campus


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