Week of October 4 - 7

Tuesday, October 5

 2:30 ALGEBRA SEMINAR  - Room 447 MSC
      Steve McCleary, Mathematics and Statistics, BGSU 
      "Lattice-ordered permutation groups, Part III"
       This is the third in a series of talks.

 2:30 SCIENTIFIC COMPUTATION SEMINAR  - Room 459 MSC
      Craig Zirbel, Mathematics and Statistics, BGSU 
      "An introduction to wavelets, Part III"
      Abstract: Beginning with a scaling function, one can show the
        existence of a wavelet whose dilates and translates form an
        orthonormal basis for L^2.  The discrete wavelet transform
        (DWT) of a function is the set of coefficients when the
        function is written in terms of this basis.  We will discuss
        these facts and some ideas from filter banks that help to
        compute the DWT coefficients.

        Matlab programs and data files related to the first talk can
        be obtained at http://www-math.bgsu.edu/~zirbel/wavelets/

        Note that the topic of this week's analysis seminar is related.

Wednesday, October 6

 2:30 ANALYSIS SEMINAR  - Room 459 MSC
      Neal Carothers, Mathematics and Statistics, BGSU 
      "An introduction to frames, Part III"
      Abstract: The grand finale of this series of talks is Casazza's
	proof that every frame can be written as a sum of three (but
	not two) orthonormal bases.

Thursday, October 7

 1:30 LUKACS LECTURE  - Room 400 MSC
      Raju Govindarajulu, Distinguished Lukacs Professor, BGSU and
                          University of Kentucky
      "Renyi representation of order statistics"

Friday, October 8

 1:30 LUKACS LECTURE  - Room 400 MSC
      Raju Govindarajulu, Distinguished Lukacs Professor, BGSU and
                          University of Kentucky
      "Locally most powerful rank tests: random effects model"
      This will be a two-hour talk.

 3:30 Refreshments
 3:45 COLLOQUIUM  - Room 459 MSC
      Jie Chen, University of Missouri-Kansas City
      "Change-point detection and estimation"
      Abstract: In this talk, a survey of the change-point detection
        and estimation will be given.  Change-point problem primarily
        arose from the process of quality control in which one
        concerns about the outputs of a production line and wishes to
        find any departure from an acceptable standard of the product.
        The problem of abrupt changes is often encountered in various
        experimental and mathematical sciences.  From a statistical
        point of view, we wish to infer (detect) whether there is a
        statistically significant change-point in a sequence of
        chronologically ordered data.  In the case that there is a
        statistically significant change-point, we also will locate
        (estimate) the change-point.  Some major methods for the
        detection and estimation of a change-point and some results
        will be discussed in the talk.