**Instructor: **Eric Slud, Statistics program, Math. Dept.

**Office: ** Mth 2314, x5-5469, email evs@math.umd.edu, **Office Hours:** M11, W1,
or by appointment

**Course Text:** J.-K. Kim and J. Shao, *Statistical Methods*, CRC 2013.

**Recommended Texts**:

*Statistical Analysis with Missing Data* (2002),
2nd edition, Wiley. *Handbook of Missing Data Methodology* (2014), Chapman and Hall.

**Overview:** This course covers the statistical analysis of data in which important components
are unobservable or missing. Such data arise frequently in large databases, in sample surveys, and even
in carefully designed experiments. By their nature, such data must be handled through the use of modeling
assumptions, generally of the form that unseen data values or their relationships with observable data
must in some way be similar to corresponding observed data values. So one of the first tasks in studying
the topic of missing data is to understand various statistical models and concepts for mechanisms of
missingness. This is where the well-known terminology of `ignorable' missingness or mechanisms of
`missing at random' come in, but also where modeling concepts of `patterns of missingness' and
`propensities' to be observed are also directly relevant.

**NOTE ON USE OF THEORETICAL MATERIAL. **Both in homeworks and the in-class test, there will
be theoretical material at the level of probability theory needed to apply the law of large numbers and
central limit theorem, along with the `delta method' (Taylor linearization) and other manipulations at
advanced-calculus level.

**Prerequisite: **Stat 420 or Stat 700, plus some computing familiarity.

**Course requirements and Grading:** there will be 5 graded homework sets (one every 2--2.5 weeks)
which together will count 2/3 of the course grade, and a final project or presentation (10-12 page paper)
that will count 1/3 of the grade.

**NOTE ON COMPUTING. **Both in the homework-sets and the course project, you will be required
to do computations on real datasets well beyond the scope of hand calculation or spreadsheet programs.
Any of several statistical-computing platforms can be used to accomplish these: **R**, SAS, Minitab,
Matlab, or SPSS, or others. If you are learning one of these packages for the first time, I recommend
**R** which is free and open-source and is the most flexible and useful for research statisticians.
I will provide links to free online **R** tutorials and will provide examples and scripts and will
offer some **R** help.

**Getting Started in R and SAS.** Lots of R introductory materials can be found on my last-year's
STAT 705 website. Another free and interactive site I
recently came across for introducing R to social scientists is: https://campus.sagepub.com/blog/beginners-guide-to-r.

Various pieces of information to help you get started in using SAS can be found under an old (F09) course
website Stat430. In particular you can find:

--- an overview of the minimum necessary steps to use SAS from Mathnet.

--- a series of SAS logs with edited outputs for illustrative examples.

FINAL PROJECT ASSIGNMENT, due Friday, May 17, 2019, 5pm. As a final course
project, you are to write a paper including some 5-10 pages of narrative, plus relevant code and graphical or tabular
exhibits, on a statistical journal article related to the course or else a data analysis or case-study based on
a dataset of your choosing. The guideline is that the paper should be 10--12 pages if it is primarily expository
based on an article, but could have somewhat fewer pages of narrative if based on a data-analytic case study.
However, for the latter kind of paper, all numerical outputs should be accompanied by code used to generate them,
plus discussion and interpretation of software outputs and graphical exhibits. For a data-analysis or case study,
the paper should present a coherent and reasoned data analysis with supporting evidence for the model you choose
to fit, the method and approach to handling missing data, and an assessment of the results.

Possible topics for the paper include: **under construction**.

(1) A handout from Stat 705 on ML estimation using the EM (Expectation-Maximization) algorithm along with another on MCMC (Markov Chain Monte Carlo) techniques.

(2) ** more under construction, including pointers to an upcoming R Scripts directory.**

Additional Computing Resources. There are many
publicly available datasets for practice data-analyses. Many of them are taken from journal articles
and/or textbooks and documented or interpreted. A good place to start is Statlib. Datasets needed in the course
will be either be posted to the course web-page, or indicated by links which will be provided here.

A good set of links to data sources from various organizations including Federal
and international statistical agencies is at Washington
Statistical Society links.

**First Class: Mon., January 28, 2019****Spring Break March 17--24, 2019****Change credit level or drop without W, February 8, 2019****Last schedule-adjustment Date (for Drop/Withdrawal): April 12, 2019****Last day of classes: Mon. December 10, 2018**

**The UMCP Math Department home page.
The University of Maryland home page.
My home page.
© Eric V Slud, Dec. 14, 2018.**