Bin Wang

University of South Alabama

Comparisons of microRNA microarray platforms and normalization methods

Microarray technology has been used to measure the messenger RNA (mRNA) expression in gene expression profiling studies. In recent years, this technology has been applied in microRNA (miRNA) discovery. In this study, we profile miRNAs from a panel of osteosarcoma xenografts individually using LNA microarray, beads-based array and TLDA cards, and compare the consistency of these three platforms. Several new miRNA normalization methods will also be investigated, and their performance will be evaluated by comparing with some existing normalization methods.

Wednesday, September 30, 2009 - 3:30pm

Danny Pfeffermann

Hebrew University, Southampton Statistical Sciences Research Institute

New Bootstrap Bias Corrections with Application to Estimation of Prediction MSE in Small Area Estimation

We develop a new method for bias correction of correct order, which models the error of the target estimator as a function of the corresponding bootstrap estimator, and the original estimators and bootstrap estimators when estimating the parameters governing the model underlying the sample. This is achieved by considering a large set of plausible parameter values, generating pseudo original samples and bootstrap samples for each parameter and then searching for an appropriate functional relationship.

Tuesday, August 25, 2009 - 3:30pm

Kevin Dobbin

Epidemiology and Biostatistics, University of Georgia

Towards a faster method for constructing a confidence interval for a classifier's accuracy in high dimensions

There are now several methods for constructing confidence intervals for prediction accuracy in high dimensional settings. But these methods have high computational cost and are cumbersome to implement. As a result, these types of intervals are rarely reported, and their properties are not well understood. In this talk, we review these methods, one in some detail, and introduce current work which utilizes a mathematical modeling approach to try to reduce the computational cost.

Thursday, August 20, 2009 - 3:30pm

Hua Liang

University of Rochester Medical Center

Variable Selection in Semi-parametric Regression Modeling

We are concerned with how to select significant variables in semi-parametric modeling. Variable selection for semi-parametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for parametric portion. Thus, it is much more challenging than that for parametric models such as linear models and generalized linear models because traditional variable selection procedures including stepwise regression and the best subset selection require model selection to nonparametric components for each sub-model.

Thursday, April 22, 2010 - 3:30pm

Xihong Lin

Harvard School of Public Health

Statistical Issues and Challenges in Analyzing High-throughput 'Omics Data in Population-Based Studies

With the advance of biotechnology, massive "omics" data, such as genomic and proteomic data, become rapidly available in population based studies to study interplay of genes and environment in causing human diseases. An increasing challenge is how to analyze such high-throughput "omics" data, interpret the results, make the findings reproducible. We discuss several statistical issues in analysis of high-dimensional "omics" data in population based "omics" studies.

Tuesday, April 20, 2010 - 3:30pm

Lijian Yang

Michigan State University

A simultaneous confidence band for sparse longitudinal regression

Functional data analysis has received considerable recent attention and a number of successful applications have been reported. In this paper, asymptotically simultaneous confidence bands are obtained for the mean function of the functional regression model, using piecewise constant spline estimation. Simulation experiments corroborate the asymptotic theory. The confidence band procedure is illustrated by analyzing the CD4 cell counts of HIV infected patients. This talk is based on Ma, S., Yang, L. and Carroll, R. (2010)

Tuesday, April 13, 2010 - 3:30pm

Rong Chen

Rutgers University

Constrained Sequential Monte Carlo

The sequential Monte Carlo (SMC) methodology has shown a great promise in solving a large class of highly complex inference and optimization problems. Although it was originally designed to solve on-line filtering and smoothing of non-linear non-Gaussian state space models, it has been shown to be equally powerful in dealing with fixed-dimensional problems, utilizing a sequential decomposition principle. In this talk we discuss issues and efficient implementations of SMC for dealing with high dimensional distributions that are defined on restricted and ill-shaped spaces.

Thursday, April 8, 2010 - 3:30pm

Travis Glenn

University of Georgia

Advances in DNA sequencing, genotyping, and microarray technologies

Advances in DNA sequencing, genotyping, and microarray technologies are providing new opportunities in all areas of biology. The rate of data increase and cost decrease over the past 2 decades has exceeded Moore's law, resulting in ever larger datasets in the hands of increasing numbers of researchers. Thus, the need for new statistical and other analytical tools is increasing tremendously. I will present information about the types of genetic and genomic data that are being collected in general and at UGA specifically.

Tuesday, March 30, 2010 - 3:30pm

Mary Meyer

Colorado State University

Constrained Penalized Splines with Applications

Penalized splines are a popular method for nonparametric function estimation in partial linear generalized regression models. Constrained versions are presented in this talk, which are useful if the function is known to be increasing or convex. The shape assumptions often fall into the category of a priori knowledge, but occasionally the research question might concern the shape. A model-selection criterion for determining if the constraints hold is shown to have nice large-sample properties and to perform well in small samples. Several applications are presented.

Friday, March 26, 2010 - 3:30pm

Jean Opsomer

Colorado State University

Cross-validation in survey estimation: model selection and variance estimation

We propose a cross-validated version of the design-based variance estimator of survey estimators, and describe its use in several survey applications. The estimator is based on the same "leave-on-one" principle as traditional cross-validation, but takes the design effects on the variance into account. We apply the cross-validated estimator as a design-based model selection tool for regression estimators, and show that it is effective in minimizing the asymptotic design mean squared error of regression estimators, both those using parametric and nonparametric models.

Thursday, March 25, 2010 - 3:30pm


Subscribe to RSS - Colloquium