Fall 2009 Courses
  • Center for Applied Statistics 560: Visiting Scholar Statistical Research Seminar
    Meeting time TBD. Special meeting times to be announced.
    • Course Description: This course brings distinguished academic statisticians to Washington University as part of an organized research seminar. Lacking a Statistics Department or Ph.D. program in statistics, the campus community can substantially benefit from internationally recognized scholars in the field who are willing to spend substantial time at the University. For the Fall of 2009, the instructor will be Brad Carlin (University of Minnesota) and the topic will be Bayesian Spatial Data Analysis. For the Spring of 2010, the instructor will be Gary King (Harvard University) and the topic will be Causal Inference.

      Selected statisticians will come to campus twice during the course. First, they will spend two days at the beginning of the semester to introduce a research topic in statistics and to assign a reading list of 8-12 technical papers, including some of their own authorship. Second, they will return to campus towards the end of semester for four days for: two 2-hour seminars, a scholarly talk in the Center for Applied Statistics, and individual meeting time with seminar participants and other members of the university community. Inbetween these two visits, a faculty member in the Center for Applied Statistics will lecture and lead a discussion on each of these assigned papers as part of the weekly seminar meeting. The objective is provide deep understanding of a complex technical topic through the use of experts in the field.

    • Prerequisite Details: The only official prerequisite for this course is a course on linear models. This may be satisfied with Math 439, Political Science 581, Epidemiology/biostat L24-439, Economics 413, M117 #524 or an approved equivalent course. Generally these courses follow from, and require, introductory statistics courses as prerequisites. The course will also assume a working knowledge of calculus and linear algebra at the level of Essential Mathematics for Political and Social Research. Jeff Gill, 2006, Cambridge University Press. Since students come to the course with varying levels of experience with statistical packages like R, some may need a bit of effort to learn basic programming skills. If you suspect that you are in this group, it will pay to spend some time with a basic text such as An R and S-Plus Companion to Applied Regression. John Fox, 2002, Sage.

  • Center for Applied Statistics 430: Multilevel Models in Quantitative Resesearch
    Thursday, 4:00-6:00PM, in Holden Hall (Washington University School of Medicine).
    • Course Description: This course covers statistical model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modeling of variation between defined groups. The course begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured. The focus on the course will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and bugs environments.

    • Prerequisite Details: This course assumes only a knowledge of basic statistics as taught in a first year graduate sequence. Topices should include: probability, cross-tabulation, basic statistical summaries, and linear regression in either scalar or matrix form. Knowledge of matrix algebra is convenient but not required. The course will make extensive use of the R statistical language and we will be offering a 2.5-day "R Boot Camp" training in late summer. Tentative dates are August 12-14 or August 19-21, with Wednesday and Thursday being full days (9-5) and Friday being one half day (9-1). Tentative schedule is as follows:
      1. Wednesday 9-12, 1-5: Basics of data handling and data analysis
      2. Thursday 9-12, 1-5: running models: linear, log-linear, tabular, and dichotomous outcomes
      3. Friday 9-1: Advanced topics for those who are interested: setting up basic simulations, model diagnostics, nonparametrics/smoothing


    • Syllabus.

  • Political Science 213: Quantitative Analysis in Political Science II
    Monday, 1:00-3:00PM in TBD.
    • Course Description: More advanced topics in the use of statistical methods, with emphasis on political applications. Topics include: properties of least squares estimates, problems in multiple regression, and advanced topics (probit analysis, simultaneous models, time-series analysis, etc." What this really means.... This course extends what you did in the linear models course by focusing more on nonlinear model forms. These are typically called "generalized linear models," although for historical reasons people in political science call them "maximum likelihood models." The principle we will care about is how to modify the standard linear model that you know so that a broader class of outcome variables can be accomodated. These include: counts, dichotomous outcomes, bounded variables, and more. The second aspect of the course is focused on the statistical package *R*

    • Prerequisite Details: The only official prerequisite for this course is a course on linear models. For political science graduate students, Political Science 581 is adequate. However, each student should be familiar with: basic probability theory, statistical inference, hypothesis testing, and least squares estimation. The course will also assume a working knowledge of calculus and linear algebra at the level of Essential Mathematics for Political and Social Research. Jeff Gill, 2006, Cambridge University Press. Since students come to the course with varying levels of experience with statistical packages like R, some may spend quite a bit of time learning basic programming skills. If you suspect that you are in this group, it will pay to spend some time with a basic text such as An R and S-Plus Companion to Applied Regression. John Fox, 2002, Sage.

    • Syllabus.


  Summer 2009 Courses
  • ICPSR: Advanced Bayesian Methods (with Skyler Cranmer, Jong Hee Park, and Patrick Brandt).
    This course covers the theoretical and applied foundations of Bayesian statistical analysis at a level that goes beyond the introductory course at ICPSR. Therefore knowledge of basic Bayesian statistics (such as that obtained from the Introduction to Applied Bayesian Modeling for the Social Sciences workshop) is assumed. The course will consist of four modules. First, we will discuss Bayesian stochastic simulation (Markov chain Monte Carlo) in depth with an orientation towards deriving important properties of the Gibbs sampler and the Metropolis Hastings algorithm. Extensions and hybrids will be discussed. Second, the course will cover model checking, model assessment, and model comparison, with an emphasis on computational approaches. The third module introduces the Bayesian approach to modeling time series data. This includes basic forms as well as recent developments such as Bayesian vector autoregression methods. The fourth week will focus on Bayesian item response theory (IRT) models, looking at theoretical foundations as well as practical issues such as identification and specification of hierarchies. Throughout the workshop, estimation with modern programming software (R, C, C++, and WinBUGS) will be emphasized.



  • Essex: Hierarchical Model Specification in Quantitative Resesearch This course covers statistical model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modeling of variation between defined groups. The course begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured. The focus on the course will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and bugs environments.