Instructor: Eric Slud, Statistics program, Math. Dept.
Office: Mth 2314, x5-5469, email evs@math.umd.edu,
Course Text: J.-K. Kim and J. Shao, Statistical Methods for Handling Incomplete Data, CRC 2013.
Recommended Texts:
Please fill out the on-line Evaluation form on this Course and instructor at http://CourseEvalUM.umd.edu. Thank you.
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.
Notes and Guidelines. Homeworks should be handed in as hard-copy in-class, except for occasional due-dates on Fridays when you may submit them electronically, via email, in pdf format. Solutions will usually be posted, and a percentage deduction of the overall HW score will generally be made for late papers.
Assignment 1. (First 2 weeks of course, HW due Mon., Feb. 11). Read about missing-data likelihoods and the definition of Missing at Random and Missing Completely at Random (material in Chapter 2 of Kim & Shao). Then solve and hand in the following problems (counting as 8 problem parts, worth 10 points for each part):
(1) Simulate Xi ~ N(1,1) independent identically distributed
(iid) , i=1,...,200, and Yi = 2 + 0.6 Xi + εi
, where εi are iid N(0,1) and independent of {Xi}, and retain as
observations only those data-pairs (Xi , Yi) for which Xi > 0 .
(2) Suppose that random variables Xi ∈ (0,1] are iid with unknown distribution F, and
that observation Xi is observed with probability w(Xi) = Xi2.
Hint: because Xi is discrete with m distinct values, you can
view the vector of its probability masses at the first m-1 of them as an unknown finite-dimensional parameter, and
calculate Fisher-information matrices, etc. Note that to prove efficiency, you must either calculate (large-sample
asymptotic) variance of your estimator as being the same as the inverse of Fisher Information, or else show that
your estimator is equivalent to (i.e., differs o(1/sqrt(n)) in probability from) the MLE.
(3) Exercise 7 in Kim and Shao Chapter 2, p.22: Consider a bivariate variable
(Y1,Y2) where (Y1,Y2) takes on possible values (1,1), (1,0), (0,1) and (0,0) with respective
probabilities π11, π10, π01 and π00,
where π11+π10+π01+π00=1. To answer
the following questions, it may be helpful to define marginal and conditional probabilities
in a 2x2 table by π1+=P(Y1 =1), π1|1=P(Y2 =1|Y1 =1) and
π1|0=P(Y2 =1|Y1 =0). Note that there is a one-to-one correspondence between
the two alternate parameterizations θ1 =
(π00, π01, π10) and θ2 =
(π1+, π1|1, π1|0). The realized sample observations
are counts nij,H and ni+,K of configurations (Y1=i,Y2=j) for the
combination of two independent data samples H and K of sizes 300 for H and 100 for K:
Assignment 2. (2nd 2 weeks, HW due Fri.,
March 1). Finish Chapter 2, Sec.2.4, and Chapter 3 through Sec.3.5 on EM Algorithm and
Monte Carlo variants in the Kim and Shao book. The problems to solve and hand in are the following
(7 parts total) : Assignment 3. (HW3 due Fri.,
March 15). Finish Chapter 3 (Sec.3.6), plus Ch.4 on Imputation through Sec.4.5 in the Kim and Shao book.
The problems to solve and hand in are the following (8 problem-parts or 80 points total): #4, 5, 7, 12 from
Chapter 3, pp.54-58. Assignment 4. (HW4 due Fri.,
April 12). Ch.4 on Imputation through Sec.4.5 in the Kim and Shao book.
The problems to solve and hand in are the following (8 problem-parts or 80 points total):
#6, 7, 10 from Chapter 4, pp. 95-97. See coursemail message for formula hints on
problems 6, 10. Assignment 5. (HW5 due Fri.,
May 10 in class). Ch.5 on Propensity Scoring through Sec.5.5 in the Kim and Shao book.
The problems to solve and hand in are the following (8 problem-parts or 80 points total): Getting Started in R and SAS. Lots of R introductory materials can be found on my last-year's
STAT 705 website. --- 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. Good topic choices for the paper include: (1) Parts of the documentation or related papers on the
mice software by van Buuren, as linked under Handouts (5) below; (2) A highly cited paper by Rebecca Andridge,
A Review of Hot Deck Imputation for Survey Nonresponse;
or (3) any subject-matter-related paper on Propensity Weighting (e.g. a famous seminal paper by Rosenbaum,
1983 Biometrika, or later papers by various authors) or Causal Inference (many possible sources
including a famous 1976 paper by Rubin) or Inverse Probability Weighting methodology. (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) R scripts related to various topics in the course can be found in the new
web-page directory RScripts.
(3) A journal paper I wrote related to combining estimators from different
samples is related also to the "Generalized Least Squares" method cited in Ch.3 of Kim and Shao in
missing-data contexts.
(4) A talk I gave in the UMD Statistics Seminar in March 2019 about Bayesian computing in a
generalized logistic mixed-model setting may be of
interest here in the context of MCEM Metropolis-Hastings algorithms.
(5) The journal paper that started the idea of "Chained Imputation". Additional readings on this topic can be found associated with the MICE R-package, and in the "Fully Conditional Specification" Chapter (Ch.13) in the CRC Missing Data Handbook under the authorship of Stef van Buuren. See an online version of it, especially Chapter 4 containing th essence pf the "fully conditional specification idea", expanding on the SRMI idea of Raghunathan
et al. You can look at a hands-on introduction to the MICE software in the pdf of a Journal of Statistical Software article of van Buuren at https://www.jstatsoft.org/article/view/v045i03.
(6) A hands-on, purely applied introduction to propensity weighting and matching using a few R
packages can be found here. 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. The UMCP Math Department home page.
See R code in this handout for coding examples related to the EM algorithms
in the pdf handout.
(a) (15 points) Impute the missing values multiple times using a randomized hot-deck
(within 8 groups defined by cross-classifying observations using the two binary values of X1
and quartiles of X2). Find estimates and (using Rubin's rules) standard errors.
(b)(10 points) Impute the missing values column-wise, multiple times using estimated univariate
(for each Ymat3[,j]) regression models on Xmat, and show that the correlations among complete-data Y
columns are very badly estimated by the multiply imputed data.
(c) (15 points) Show theoretically that the model for Y[,2] on Y[,1] in the data subset consisting
of the first 2 columns disregarding Xmat is not MAR. (That is, show that the conditional
distribution of Y[i,2] given Y[i,1] is different for the i's with Ri = 1 and those for
Ri=0.)
π(Xi,φ) = exp(φ0+Xi %*%
(φ1,...,φ1)/pw[i] solving the weighted-propensity estimating equation
(ie calibration equation) in terms of the same 4 non-constant predictor variables.
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:
Possible topics for the paper include: implementation and analysis/interpretation of one or more imputation
methods on a real dataset (e.g., survey public-use data from American Community Survey) using methods and software
discussed in the course; exposition of a journal paper on missing data methods in a subject-matter application,
such as educational statistics; exposition of some other missing-data topic, such as double- or interval-censored
data, from a paper or book-chapter; or some other topic you propose.
Handouts
A good set of links to data sources from various organizations including Federal
and international statistical agencies is at Washington
Statistical Society links.Important Dates
The University of Maryland home page.
My home page.
© Eric V Slud, May 6, 2019.