Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Donglin Zeng

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…


Guanqun Cao

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…


Chunming Zhang

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. This paper provides empirical evidence that for a range of commonly used control levels, the conventional FDR procedure can lack the ability to detect statistical significance, even if the p-values under the true null …


Runze Li

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…


Peihua Qiu

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…


Andy Bartlett

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…


Xinyu Liu

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…


Zaid Abdo

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,…


Nat Kulvanich

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…


Jian Huang

TBD Joint seminar with the Department of Epidemiology and Biostatistics.
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