Week of January 26 - 30

Monday, January 26

10:30 GRADUATE STUDENT SEMINAR  - Room 459 MSC
      Organizational meeting.  Contact Norm Preston (npresto@BGNet)

11:30 MATHEMATICS EDUCATION SEMINAR  - Room 447 MSC
      Barbara Moses, Dept. of Mathematics and Statistics, BGSU. 
      "What is constructivism?  How does student learning improve by
       using a constructivist approach?"

 2:30 ALGEBRA SEMINAR  - Room 447 MSC
      Curt Bennett, Dept. of Mathematics and Statistics, BGSU. 
      "Buildings: an introduction"

 2:30 ANALYSIS SEMINAR  - Room 459 MSC
      Alex Izzo, Dept. of Mathematics and Statistics, BGSU. 
      "The d-bar equation"

 3:30 STATISTICS SEMINAR SERIES  - Room 459 MSC
      Jiahua Chen, University of Waterloo, visiting BGSU this semester
      "Empirical Likelihood Methods"

Tuesday, January 27

 2:30 SCIENTIFIC COMPUTATION SEMINAR  - Room 459 MSC
      Gordon Wade, Dept. of Mathematics and Statistics, BGSU. 

 2:30 MAPLE WORKSHOP  - Scientific Computing Lab, MSC
      John Gresser, Dept. of Mathematics and Statistics, BGSU. 
        After the usual start up issues (meeting time, format, etc.)
        I'll give a quick lecture on Maple basics to start things off.

Wednesday, January 28

11:30 MATHEMATICS EDUCATION SEMINAR  - Room 447 MSC
      Barbara Moses, Dept. of Mathematics and Statistics, BGSU. 
      "What is constructivism?  How does student learning improve by
       using a constructivist approach?"
      
 8:00 GUEST SPEAKER  - Room 459 MSC
      Piotr Gasiewski, Price Waterhouse LLP

Thursday, January 29

 1:00 STATISTICAL COMPUTING SEMINAR  - Room 459 MSC
      Jim Albert, Dept. of Mathematics and Statistics, BGSU. 
      "Likelihood Inference"

Friday, January 30
      
 3:30 Coffee
 3:45 COLLOQUIUM  - Room 459 MSC
      Jiahua Chen, University of Waterloo, visiting BGSU this semester
      "Biases and variances of survey estimators based on nearest-neighbor
       imputation"
      Abstract: Nearest neighbor imputation is one of the hot deck
        methods used to compensate for nonresponse in sample surveys.
        Although it has a long history of application, theoretical
        properties of the nearest neighbor imputation method are
        unknown.  In this paper we show that under some conditions,
        the nearest neighbor imputation method provides asymptotically
        unbiased and consistent estimators of functions of population
        means and totals, and population distributions and quantiles.
        We also derive the asymptotic variances for estimators based
        on nearest neighbor imputation and consistent estimators of
        these asymptotic variances.  Some simulation results show that
        the estimators based on nearest neighbor imputation and the
        proposed variance estimators have good performances.

        Joint work with Jun Shao, University of Wisconsin