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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