Ping Ma

Nonparametric Analysis of RNA-Seq

With the rapid development of second-generation sequencing technologies, RNA-Seq has become a popular tool for transcriptome analysis. It offers the chance to detect novel transcripts by obtaining tens of millions of short reads. After mapped to the genome and/or to the reference transcripts,   RNA-Seq data can be summarized by a tremendous number of short-read counts. The huge number of short-read counts enables researchers to make transcript quantification in ultra-high resolution.

Tuesday, April 10, 2012 - 3:30pm

Ji Meng Loh

K-scan for anomaly detection in spatial point patterns

We consider the problem of detecting hotspots in spatial point patterns observed over time while accounting for an inhomogeneous background intensity. For example, in disease surveillance, the interest is often in identifying regions of unusually high incidence rate given a background incidence rate that may be spatially varying due to underlying variation in population density, say. I will present a K-scan method that uses components of the inhomogeneous K function to identify such anomalies or hotspots.

Thursday, April 19, 2012 - 3:30pm

R. Dennis Cook

Envelope Models and Methods

We will discuss a new approach to estimation in the classical multivariate linear model that yields estimators of the coefficient matrix with the potential to be substantially less variable asymptotically than the standard estimators. The new approach arises by recognizing that the response vector may contain information that is immaterial to the purpose of estimating the coefficients, but can still introduce substantial extraneous variation into estimation.

Friday, April 13, 2012 - 4:30pm

Yongtao Guan

Optimal estimation of the intensity function of a spatial point process

Although optimal from a theoretical point of view, maximum likelihood estimation for Cox and cluster point processes can be cumbersome in practice due to the complicated nature of the likelihood function and the associated score function. It is therefore of interest to consider alternative more easily computable estimating functions. We derive the optimal estimating function in a class of first-order estimating functions. The optimal estimating function depends on the solution of a certain Fredholm integral equation and reduces to the likelihood score in case of a Poisson process.

Thursday, April 5, 2012 - 3:30pm

Tailen Hsing

Nonparametric Estimation of the Variogram and its Spectrum

In the study of intrinsically stationary spatial processes, a new nonparametric variogram estimator is proposed through its spectral representation.  The methodology is based on estimation of the variogram's spectrum, here for the isotropic case, which is formulated in terms of solving a regularized inverse problem. We use quadratic programming to obtain the solution. The estimated variogram is guaranteed to be conditionally negative-definite, a key property of variograms.

Thursday, March 29, 2012 - 4:00pm

Chanseok Park

Parametric Reliability Estimation from Load-Sharing System Data

In this talk, we will consider parametric reliability estimation for load-sharing systems under a load-sharing rule. Consider a system of multiple components connected in parallel.  In this system, as components fail one by one, the total load or traffic applied to the system is re-distributed among the remaining working components. This is commonly referred to as load-sharing.  We will review the earlier work on load-sharing models and then discuss the problem of estimating load-sharing parameters.

Thursday, March 22, 2012 - 3:30pm

Jian Kang

Some new spatial point process models for functional neuroimaging meta analysis

In this talk, we focus on some new spatial point process models with their applications to meta analysis of functional neuroimaging data. We propose a Bayesian spatial hierarchical model using a marked independent cluster process for functional neuroimaging meta analysis. In contrast to the current approaches, our hierarchical model accounts for intra-study variation in location (if any), inter-study variation, and idiosyncratic foci that do not cluster between studies.

Thursday, March 8, 2012 - 3:30pm

Regina Liu

DD-Classifier and Other Applications of DD-Plots

Data depth and its induced center-outward ordering have given rise to many useful tools in nonparametric multivariate analysis. A DD-plot (depth vs depth plot) is the two dimensional scatter plot of depth values of the given sample points with respect to the two underlying distributions. It can be a useful tool to visualize the difference of two distributions. We discuss some of the utilities of DD-plots in this presentation. In particular, we discuss approaches devised from DD-plots to classification (thus named DD classifier) and testing the difference between two samples.

Thursday, March 1, 2012 - 3:30pm

Wenxuan Zhong

COP: a forward selection procedure for the index model

In this talk, I will present the Correlation Pursuit (COP) method, a variable selection procedure developed under the sufficient dimension reduction framework. Unlike the conventional stepwise, COP does not impose a special form of relationship between the response variable and the predictor variables. The COP procedure selects variables that maximize the correlation between the transformed response and linear combinations of the predictors.

Thursday, February 23, 2012 - 3:30pm

Xuming He

Bivariate Downscaling for Climate Projections

Statistical downscaling is a useful technique to localize global or regional climate model projections to assess the potential impact of climate changes. It requires quantifying a relationship between climate model output and local observations from the past, but the two sets of measurements are not necessarily taken simultaneously, so the usual regression techniques are not applicable. In the case of univariate downscaling, a simple quantile-matching approach with asynchronous measurements often works well, but challenges remain for downscaling bivariate data.

Thursday, February 16, 2012 - 3:30pm


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