Monday, April 9, 2012 Tuesday, April 10, 2012 Wednesday, April 11, 2012 1:30 PM Math 1150 Meeting 447 MSC 4:30 PM Analysis Seminar 459 MSC Swarup Ghosh, BGSU A Counterexample to the Peak Point Conjecture (Part I) In 1957, Andrew Gleason conjectured that for a function algebra A on X, if every point in X is a peak point for A, then A = C(X), known as peak point conjecture. However, in 1968, in his Ph.D. dissertation, Brian Cole constructed a counterexample to this conjecture. In this talk, we will discuss Cole's counterexample and related conjectures. Thursday, April 12, 2012 11:00 AM Math 1150 Meeting 459 MSC 2:30 PM Putnam Team Meeting 445 MSC 2:30 PM Graduate Student Seminar 459 MSC Swarup Ghosh, BGSU Maximal ideal space of rings of continuous functions of compact topological spaces (Part II) In the theory of rings of continuous functions, it is obvious that the ring structure of C(X), the collection of all real valued continuous functions on a topological space X, is completely determined by the properties of the space X. An important problem is to specify conditions under which, conversely, X determined as a topological space by the algebraic structure of C(X). We shall present a result that represents one of the milestones in the development of this theory: within the class of compact spaces, the ring structure of C(X) determines X up to homeomorphism. 7:00 PM BGSU student actuarial science club 224 MSC The BGSU student actuarial club hosts Cincinnati Financial, an actuarial employer. Everyone interested in learning about actuarial careers is welcome; free pizza and soft drinks will be provided. Friday, April 13, 2012 3:30 PM Refreshments 459 MSC 3:45 PM Colloquium 459 MSC Dr. Shiming Zheng, College of Public Health, East Tennessee State University Random Regression Models for the Longitudinal Data Analysis Subject to Left Censoring and Informative Drop-outs and Based On The Skew Elliptically Contoured Distribution Assumptions The class of skew ECDs is large and accommodates distributions which are both symmetric and asymmetric, with both heavy tails and thin tails. It also accommodates distributions with large range of skewness and with different levels of kurtosis such as leptokurtic and platykurtic or mesokurtic distributions. Under the skew ECD assumptions, the outcome variables can be modeled and predicted more accurately and precisely, the (1-alpha)100% prediction confidence intervals are narrower and the estimators are more robust if we deal with some skewed data. First we extend the distribution assumption from the normal to the ECD for random regression models used in analysis of longitudinal data accounting for both undetectable values and informative drop-outs. Then we extend further to the skew ECD distribution assumption for the same models. For the unimodal asymmetric continuous data, the skew ECD models fit the data better than the ECD models, which are better than classical normal models. To illustrate usefulness of our models we use the data from the HIV Epidemiology Research Study (HERS).
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