Eric V. Slud Professor, Statistics Program Department of Mathematics University of Maryland College Park, MD 20742 

Research
Interests Info on Older RIT's Links 
Office hours: M 11am and W 1:30pm, or by appointment (MWF only)
Past Teaching: MiniCourses
MiniCourse on CrossClassified Factor
Analysis
Lecture
(10/17/05) Mathematical Challenges in CrossClassified
Factor Analysis
Summary of interesting mathematical issues
related to PhD theses about Factor Analysis
by my former students
Yang Cheng(2004) and Sophie (HsiaoHui) Tsou (2005).
MiniCourse on Markov Chain Monte Carlo
(Statistical Simulation Techniques)
Spring '04: 4/21, 4/28
Slides can be found at links indicated under each
lecture below.
The lecture topics are as follows:
Lecture
1 (4/21/04) Metropolis Hastings Algorithm 
Motivation from AcceptReject
simulation methodology and from Markov
Chain theory. Extended example and issues
involved in the choice of `proposal
Markov chain' from which the MetropolisHastings
chain is built. Gibbs Sampler motivation.
Lecture
2 (4/28) Recap of Gibbs Sampler
motivation. Testing for Markov Chain Monte Carlo
convergence from the internal evidence
of the Gibbs Sampler trajectory. Statistical examples:
Bayesian statistical computation and
frequentist treatment of hierarchical statistical models.
MiniCourse on Statistics of Survival Data
(Fall '02: 11/6, 11/13, 11/20)
Slides can be found at links indicated under each
lecture below.
The lecture topics are as follows:
Lecture 1
(11/6/02) Survival Times, Death Hazards & Competing Risks
Lecture 2
(11/13) Population Cohorts and Martingales
Lecture 3
(11/20) Survivaldata likelihoods with InfiniteDimensional
Parameters
(1) Fall '05 RIT on
Statistics of Models with Growing Parameter Dimension
(2) Spring '04 RIT on MetaAnalysis. Click
here
for webpage.
Briefly,
metaanalysis concerns the simultaneous statistical analysis
of a number of
related studies or datasets within a single statistical model. The fact
that parameters are
shared across datasets (e.g. a treatmenteffectiveness parameter assumed
constant
across a number of separately conducted clinical studies of
the effectiveness of the same
treatment regimen for the same disease) allows the possiblity of increasing
sensitivity or
power of statistical tests. However, such an increase in precision comes
at the price of
simultaneous model assumptions whose compatibility with the data must be
validated.
This RIT was an outgrowth of the Fall '03 RIT on Large CrossClassified
Datasets
(see webpage linked below for details).
(3) Spring and Fall '03 RIT on Statistics of Large
CrossClassified Datasets:
see
RITF03
webpage .
(4) Intensive Seminar, Fall 2002. See
plan
for details.
In Fall 2002, I ran a
`research interaction' seminar including my own
graduate advisees
and others, on the mathematical & statistical
topics which more broadly correspond to
the overlap
of my students' thesis projects and most of my own current
research interests,
namely Statistics of Large CrossClassified Datasets.
Roughly speaking, these are
problems in which
there is a large samplesize n, but where the predictor variables
and/or
crossclassifications of the sample units become more complicated or
numerous
as n gets large. Such problems range from Semiparametric
Statistical Inference to
Orderselection problems
in regression and time series, to Classification
and Clustering
as in the Microarray
data problems mentioned below. These problems suggest the
need for a new
Asymptotics which explicitly recognizes the growth of the parameter
space of a probability
model as a function of the size n of the dataset.
(5) Intensive Seminar, Spring 2002.
See
plan
for details.
In Spring 2002,
following up on the Fall 2001 seminar described below, I ran an intensive
seminar on
statistical analysis of DNA Microrarrays, for students considering
research
in this
area. Dataanalysis figured prominently, performed by me and
also by two of the
several graduate students who participated.
(6) Genomics/Microarray
Seminar Fall 2001, AMSC 699:
Mathematical Topics in
Functional Genomics. Click
here
for the reading list.
Other Past Teaching and Seminars
(1)
Spring '04, introductory course Stat 470
on Actuarial Mathematics, taught primarily
from book
notes which I wrote. Coverage includes theory of interest, life tables,
review of
probability
theory, expectations of timediscounted insurance costs and premiums
calculated
from life tables, and special models of mortality.
See
the old course webpage
(from which the main
text can be downloaded a chapter at a time) for further
details.
(2)
Spring '04 Stat 798C,
Computational Methods in Statistics, a graduate
introduction
to statistical computing
with emphasis on the Splus (or R) and SAS computer packages.
(I also taught this
course in Spring '03.)
(3) Fall '03, Stat 798S, topics course on Survival Analysis .
(4)
Spring '03, Stat 770 ,
a course on Analysis of Categorical Data, taught out
of the book, Categorical Data Analysis, by A. Agresti.
(5)
For slides of my Stat Seminar presentation May 3, 2012 with
Jiraphan Suntornchost, click here
(I) Census statistics, specifically demographic modelling
of nonresponse to national surveys, with particular application to
Weighting Adjustment and Small Area Estimation (SAE).
Much of my smallarea estimation work has been directed toward the SAIPE (Small
Area Income and Poverty Estimation ) program of the Census Bureau. See for example the comparative SAE study. My methodological research in this area
includes smallarea and MSE estimation from survey data satisfying nonlinearly transformed FayHerriot models or leftcensored FayHerriot models. Some further work on internal
evaluation of biases due to weighting adjustment for nonresponse in a longitudinal survey (SIPP, Survey on Income and Program Participation) is described in
my Nov. 2007 FCSM talk. A paper describing the contents of that talk more fully can be
found here, and in a form that appeared in the Journal of Official Statistics, here. Other recent work on simultaneous nonresponseadjustment and calibration of weights in complex
surveys can be found in a Census SRD Technical Report.
(II) Survival data analysis, which includes both semiparametric inference and clinical trial design issues. The semiparametric work emphasizes maximization of variants of nonparametric likelihoods, especially in Transformation and Frailty models. Further work on a general approach to efficient semiparametric estimation described in slidesfrom a talk given in the IISA Conference, June 14, 2002. Other work relates to decisiontheoretic optimal earlystopping procedures and new designs in clinical trials.
For slides of a Stat Seminar I gave in Fall '03 at NIH on asymptotic theory of Semiparametric statistical procedures in Transformation models, click here.
(III) Largescale data problems with emphasis on crossclassified data, Principal Components (paper on representation of tongue surface during speech, appeared in the journal Phonetica), and clustering. More recently, I have had two students (Yang Cheng and Sophie Tsou) obtain PhD's working on Factor Analysis models. A talk I gave on this work in 2005 [and then again in the Diffusion Wavelet RIT in Fall 2007] can be found here.
(IV) Stochastic processes. Two examples are work emphasizing highdimensional Markov processes applied to equilibria in Economics (paper in Journal of Economic Theory,
for which 2nd pdf file in directory contains Figure); to Proteinfolding; and to ascertainment of number of distinct
DNA `species' from sequencing experiments.