Yuhong Yang

Parametric or Nonparametric? An Index for Model Selection

Parametric and nonparametric models are convenient mathematical tools to describe characteristics of data with different degrees of simplification. When a model is to be selected from a number of parametric candidates, not surprisingly, differences occur when the data generating process is assumed to be parametric or nonparametric. In this talk, in a regression context, we will consider the question if and how we can distinguish between parametric and nonparametric situations and discuss feasibility of adaptive estimation to handle both parametric and nonparametric scenarios optimally.

Thursday, April 7, 2011 - 3:30pm

Peter Hall

Contemporary Frontiers in Statistics

The availability of powerful computing equipment has had a dramatic impact on statistical methods and thinking, changing forever the way data are analysed. New data types, larger quantities of data, and new classes of research problem are all motivating new statistical methods. We shall give examples of each of these issues, and discuss the current and future directions of frontier problems in statistics.

Tuesday, April 5, 2011 - 3:30pm

Bimal Sinha

Inference about a common mean of several univariate normal populations

We will provide a comprehensive review of basics of statistical meta-analysis and discuss its relevance for the problem of drawing inference about a common mean of several univariate normal populations with unknown and unequal variances. This problem, which is related to Behrens-Fisher problem, has many applications, and we will study two real data sets.

Thursday, March 31, 2011 - 3:30pm

Hao Wu

Genomic "bump finding”

Exploring genomic landscapes of different biological endpoints is an important approach for understanding biological processes and disease etiologies. Examples of these endpoints are sequence composition, DNA methylation, histone modifications, and binding sites for different transcription factors. With the completion of human genome project and advances of high-throughput technologies, tightly spaced measurements have been collected from linear chromosomes to create unbiased maps at the whole-genome scale.

Thursday, March 3, 2011 - 3:30pm

Hui Jiang

Statistical issues in high-throughput profiling of isoform-specific gene

In mammalian cells, isoforms of a gene can have highly similar sequences yet encode proteins with remarkably different functional roles. Quantifying cellular abundance of isoforms is therefore of significant biological interest. In this talk, we will review methods for profiling isoform-specific gene expression using high-throughput technologies such as microarrays and ultra high-throughput RNA sequencing (RNA-Seq). We will show the intrinsic non-identifiability issue involved in the isoform deconvolution problem, especially for microarray data.

Wednesday, February 23, 2011 - 3:30pm

Hanwen Huang

University of North Carolina at Chapel Hill

High Dimensional Statistical Learning

In this talk, I will present some new contributions to the area of high dimensional statistical learning. The focus will be on both classification and clustering. Classification is one of the central research topics in the field of statistical learning. For binary classification, we propose the Bi-Directional Discrimination (BDD) method which generalizes linear classifiers from one hyperplane to two or more hyperplanes. BDD provides a compromise between linear and general nonlinear methods.

Monday, February 21, 2011 - 3:30pm

Anna Bargagliotti

Technology, practice, and curriculum: Links to Student Achievement and Understanding

In this talk, I will begin by providing a brief overview of my mathematics and statistics education research. This work has focused on understanding how teaching practices influence student achievement. More specifically, I am interested in exploring how technology can be used to improve learning in mathematics and statistics, how teacher knowledge affects student achievement, and how curriculum influences practice.

Wednesday, February 16, 2011 - 3:30pm

Jennifer J. Kaplan

Lexical Ambiguity in Statistics: The Cases of Random and Spread

Words that are part of everyday English and used differently in a technical domain possess lexical ambiguity. The use of such words may encourage students to make incorrect associations between words they know and words that sound similar but have specific meanings in statistics that are different from the common usage definitions. This talk will present results from parts of a sequence of studies designed to understand the effects of and develop techniques for exploiting lexical ambiguities in the statistic classroom.

Thursday, February 24, 2011 - 3:30pm

Qunhua Li

Measuring Reproducibility of High-Throughput Biological Experiments

Reproducibility is essential to reliable scientific discovery in large-scale high-throughput biological studies. In this talk, I will present a unified approach to measure reproducibility of findings identified from replicate experiments and select discoveries using reproducibility between replicates.

Monday, February 14, 2011 - 3:30pm

Julie Patterson

University of Illinois - Urbana-Champaign

Cognitive Diagnosis Modeling of Introductory Statistics

Diagnosis of student mastery or non-mastery of a set of skills (or attributes) can be done using cognitive diagnosis models. Before diagnosing the students, the skills need to be chosen and the appropriate model needs to be selected. In this talk, I will first introduce the process of deconstructing the domain of an introductory statistics course into a hierarchical arrangement of cognitive attributes.

Tuesday, February 8, 2011 - 3:30pm


Subscribe to RSS - Colloquium