Jun Liu

Harvard University

Detecting Nonlinear Relationships via Slicing

I will discuss a few recent results from my group aiming to the detection of non-linear dependence and interactive effects of several random variables. These approaches were all developed by taking a Bayesian viewpoint on the inverse-slicing idea first proposed by Ker-Chau Li. We will also show how these methods are applied to bioinformatics problems such as gene-set enrichment analysis, transcriptional regulation analysis, etc.

http://en.wikipedia.org/wiki/Jun_Liu

Friday, April 22, 2016 - 3:30pm
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David Banks

Duke University

Mining Text Networks

The last decade has seen substantial progress in topic modeling, and considerable progress in the study of dynamic networks.  This research combines these threads, so that the network structure informs topic discovery and the identified topics predict network behavior.  The data consist of text and links from all U.S. political blogs curated by Technorati during the calendar year 2012.  A particular advantage of the model used in this research is that it naturally enforces cluster structure in the topics, through a block model for the bloggers.

Thursday, October 1, 2015 - 3:30pm
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David Ruppert

Cornell University

A Bayesian Multivariate Functional Dynamic Linear Model

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework.

Thursday, April 7, 2016 - 3:30pm
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Yingnian Wu

University of California, Los Angeles

Inducing Wavelets into Random Fields via Generative Boosting

We propose a learning algorithm for a class of random field models of natural image patterns, where the energy functions of the random fields are in the form of linear combinations of rectified filter responses from subsets of wavelets selected from a given over-complete dictionary. The algorithm consists of the following two components. (1) We propose to induce the wavelets into the random field model by a generative version of the epsilon-boosting algorithm.

Thursday, September 24, 2015 - 3:30pm
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Bo Li

University of Illinois at Urbana-Champaign

Statistics in Paleoclimate Reconstruction

Understanding the complex dynamics of Earth's climate system is a grand scientific challenge. Projecting climate for 50 or 100 years into the future is, however, complicated by the fact that the behavior of the Earth system over such time scales is not well characterized over the modern instrumental interval, which only stretches back about 100-150 years with global extent.

Thursday, December 3, 2015 - 3:30pm
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Debashis Paul

University of CA at Davis

Nonparametric estimation of dynamics of monotone trajectories

We propose a nonparametric estimator of the dynamics of monotonically increasing or decreasing trajectories defined on a finite time interval. Such trajectories can be described as solutions of autonomous ODEs. Under suitable regularity conditions, we derive the optimal rate of convergence for the proposed estimator and show that it is the same as that for estimating the derivative of a trajectory. We also show that commonly used two-stage estimation schemes are typically inefficient. 

http://www.stat.ucdavis.edu/~debashis/

Thursday, September 17, 2015 - 3:30pm
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Bing Li

Penn State University

Nonlinear sufficient dimension reduction for functional data

We propose a general theory and the estimation procedures for nonlinear sufficient dimension reduction where the predictor or the response, or both, are random functions. The relation between the response and predictor can be arbitrary and the sets of observed time points can vary from subject to subject. The functional and nonlinear nature of the problem leads naturally to consideration of two levels of functional spaces: the first space consisting of functions of time; the second space consisting of functions defined on the first space.

Thursday, October 15, 2015 - 3:30pm
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Max D. Morris

Iowa State University

Statistical Comparison of Striated Toolmarks

Much of forensic laboratory work is based on comparison of evidence from a crime scene with analogous material associated with a suspect.

Thursday, November 5, 2015 - 3:30pm
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Faming Liang

University of Florida

Bayesian Neural Networks for Personalized Medicine

Complex diseases such as cancer have often heterogeneous responses to treatment, and this has attracted much interest in developing individualized treatment rules to tailor therapies to an individual patient according to the patient-specific  characteristics. In this talk, we discuss how to use Bayesian neural networks to achieve this goal, including how to select disease related features.

Thursday, November 12, 2015 - 3:30pm
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