Our department is very pleased with the hiring of Dr. Mark Werner, who started his position as Lecturer in August 2010. Mark will play an important role in teaching statistics for business students, and is also interested in further pursuing his interests in statistics education. Welcome Mark!
We would also like to congratulate Dr. Kim Gilbert and Jack Morse, in that their positions are now permanent instead of temporary. Kim is a Lecturer, primarily working with MSIT 3000 classes (along with Mark), and Jack Morse is now a permanent Instructor who teaches STAT 2000 classes.
Functional magnetic resonance imaging (fMRI) is an advanced technology for studying brain functions. Due to the complexity and high cost of fMRI experiments, high quality multi-objective (MO) fMRI designs are in great demand; they help to render precise statistical inference, and are keys to the success of fMRI experiments. Here, we propose an efﬁcient approach for obtaining MO fMRI designs. In contrast to existing methods, the proposed approach does not require users to specify weights for the different objectives, and can easily handle constraints to fulﬁll customized requirements.
No abstract is available.
This paper studies off-diagonal decay in symmetric Toeplitz matrices. It is shown that if the generating sequence of the matrix is monotone, positive and convex then the monitonicity and positivity are maintained through triangular decomposition. The work is motivated by recent results on explicit bounds for inverses of triangular matrices.
Developing statistical procedures to determine the number of components, known as the mixture complexity, in finite mixture models remains an area of intense research. In many applications, it is important to find the mixture with fewest components that provides a satisfactory fit to the data. This article focuses on consistent estimation of unknown number of components in finite mixture models, when the exact form of the component densities are unknown but are postulated to be close to members of some parametric family.