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 9:3010:30am and 1pm and W 11am and 4pm for fall 2013
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) 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
slides
from a talk given in the IISA Conference, June 14, 2002. Other current
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 .
(II) 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
recent 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 to appear 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.
(III) Largescale data problems with emphasis on crossclassified data,
Principal
Components (paper on representation of tongue surface during
speech,
recently 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. Papers extending that work are now in preparation
and will be posted to this space shortly. 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, currently 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.