Liang Liu

Delaware State University

Estimating species trees from multilocus data under the coalescent model

The desire to infer the evolutionary history of a group of species (species tree) should be more viable now that a considerable amount of multilocus molecular data is available. In this talk, I will introduce three statistical methods for reconstructing species trees under the multispecies coalescent model. The Bayesian method can estimate the topology, species divergence times, and population sizes of the species tree, but involves intensive computation.

Thursday, February 10, 2011 - 3:30pm

Jeffrey S. Morris

Automated, Robust Bayesian Analysis of Functional and Quantitative Image Data using Functional Mixed Models and Isomorphic Basis-Space Modeling

In this talk, I will describe flexible new Bayesian methods to analyze functional and quantitative image data. The methods are based on functional mixed models, a framework that can simultaneously model multiple factors and account for correlation within and between the functions. I use an isomorphic basis-space approach to fitting the model, which leads to efficient calculations and adaptive smoothing yet flexibly accommodates the complex features characterizing these data.

Monday, February 7, 2011 - 4:30pm

Jaejik Kim

Validation and Selection of ODE Models for Parotid De-differentiation

Salivary glands are important for producing salivary proteins which contribute to host defense, lubrication, and digestion. However, salivary glands are often damaged or destroyed by radiation therapy or surgery for head and neck cancers, or by advanced Sjogrens syndrome. In order to engineer or replace salivary glands, it is important to define the major intracellular pathways of the nuclear program that causes terminal differentiation of the parotid acinar cells. Gene network discovery is a critical part to do this.

Thursday, January 20, 2011 - 3:30pm

Samuel Kou

Multi-resolution inference of stochastic models from partially observed data

Stochastic models, diffusion models in particular, are widely used in science, engineering and economics. Inferring the parameter values from data is often complicated by the fact that the underlying stochastic processes are only partially observed. Examples include inference of discretely observed diffusion processes, stochastic volatility models, and double stochastic Poisson (Cox) processes. Likelihood based inference faces the difficulty that the likelihood is usually not available even numerically. Conventional approach discretizes the stochastic model to approximate the likelihood.

Tuesday, March 29, 2011 - 3:30pm

Richard Samworth

Maximum likelihood estimation of a multidimensional log-concave density

If X_1,...,X_n are a random sample from a density f in , then with probability one there exists a unique log-concave maximum likelihood estimator of f. The use of this estimator is attractive because, unlike kernel density estimation, the estimator is fully automatic, with no smoothing parameters to choose. We exhibit an iterative algorithm for computing the estimator and show how the method can be combined with the EM algorithm to fit finite mixtures of log-concave densities.

Tuesday, November 16, 2010 - 3:30pm

Douglas P. Wiens

Robustness of Design in Dose-Response Studies

In work carried out with my colleague Pengfei Li, we construct experimental designs for dose-response studies. The designs are robust against possibly misspecified link functions; to this end they minimize the maximum mean squared error of the estimated dose required to attain a response in 100p% of the target population. Here p might be one particular value - p = .5 corresponds to ED50 estimation - or it might range over an interval of values of interest. The maximum of the mean squared error is evaluated over a Kolmogorov neighbourhood of the fitted link.

Thursday, November 4, 2010 - 3:30pm

Jing Wang

On Determination of Linear Components in Additive Models

Additive models have been widely used in nonparametric regression, mainly due to their ability to avoid the problem of the ``curse of dimensionality". When some of the additive components are linear, the model can be further simplified and higher convergence rates can be achieved for the estimation of these linear components. In this paper, we propose a testing procedure for the determination of linear components in nonparametric additive models.

Thursday, October 28, 2010 - 3:30pm

Liping Tong

Co-evolution Model for Dynamic Social Network and Behavior

An individual's behaviors may be influenced by the behaviors of friends, such as hours spent watching television, playing sports, and unhealthy eating habits. However, preferences for these behaviors may also influence the choice of friends; for example, two children who enjoy playing the same sport are more likely to become friends. To study the interdependence of social network and behavior, Snidjers et al.

Thursday, October 21, 2010 - 3:30pm

Myung Hee Lee

HDLSS Discrimination with Adaptive Data Piling

We propose linear discrimination methods which regularize piling of the low dimensional projections for high dimensional, low sample size data. The maximal data piling achieves the extreme regularization by yielding zero scatter within the class while maximizing the separation between the classes. Two different piling regularization methods are studied in this article. Our first attempt to regularize data piling is done by employing linear paths connecting the maximal data piling direction and least data piling direction.

Thursday, October 7, 2010 - 3:30pm


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