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Department of Statistics

News and Events

Special Topics Course:  Bayesian Statistics

In Fall 2015, Dr. Nora Bello will be conducting a special topics course in Bayesian statistics.  Bayesian data analysis a very effective and practical tool for both statisticians and subject-matter scientists. This course will be suitable for graduate students and practitioners from many disciplines, provided they have a basic background in frequentist statistics. 

View Syllabus


Wei-Wen Hsu receives A&S Award

Wei-Wen Hsu is the recipient of a Arts and Sciences Faculty Enhancement Award on Statistical Classification Models for Emerging Molecular Analysis in the Early Detection of Cancer.  

Early detection of cancers is a recurring challenge in medical research. One often cited justification is that for those very aggressive cancers such as ovarian and pancreatic cancers, there are often no significant symptoms or signs at very early stages. 

Owing to the advent of nanoplatform technology, the early detection of cancers is no longer impracticable. An in-vitro assay based on the Fe/Fe3O4-based nanoplatforms developed by Bossmann/Troyer groups at Kansas State University is particularly sensitive in protease detection and is anticipated to be successfully used in the early detection of solid tumors. This assay with a very low limit of detection (LOD) can effectively capture the protease activities in blood serum during the early stages of cancer development, which can dramatically improve the ability of identifying the early stage of cancer. However, there is no methodological, comprehensive study that rigorously evaluates the performance of this in-vitro screening assay on all types of cancers. Particularly, a general statistical classification model that can enhance the signals of the protease signatures to improve the classification rate has not been proposed and discussed.

The objective of this project is to develop a statistical methodology to study and evaluate the overall performance of the rapid screening in-vitro assay in detecting cancers. If successful, the new methodology which is expected to have a higher classification rate than other existing methods will facilitate the classification of patients at early stages of cancers using the promising in-vitro assay. The work will be conducted during the 2015-2016 academic year.