Eric V. SludProfessor, Statistics Program Department of Mathematics University of Maryland College Park, MD 20742 | | Research InterestsInfo on Older RIT's Links |

- Office: Math 2314
- Telephone: 301 405 5469
- Fax: 301 314 0827
- Email (the best way to reach me): evs@math.umd.edu

Office hours: M 11am and W 1:30pm, or by appointment (MWF only)

Stat 650, Applied Stochastic Processes

Stat 705, Statistical Computing in R

Stat 701, Mathematical Statistics II

Stat 700, Mathematical Statistics I

Stat 701, Mathematical Statistics II

RIT on Weighted Estimating Equations in Surveys and Biostatistics

Stat 700, Mathematical Statistics I

RIT on Weighted Estimating Equationsin Surveys and Biostatistics

Stat 401, Applied Prob. and Statistics II

Stat 710, Advanced Statistics -- Large-Sample Theory

Math 420, Mathematical Modeling

Stat 401, Applied Prob. and Statistics II

Stat 440, Sampling Theory

RIT on BiasedSampling

Stat 701, Mathematical Statistics II

Stat 700, Mathematical Statistics I

Stat 705, Computational Statistics (in R)

RIT onMultilevel Statistical Models (joint with P. Smith)

Stat 701, Mathematical Statistics II

Stat 430, SAS and Regression

RIT onSemiparametric Statistics

AMSC 762 DataAnalysis Seminar (joint with Paul Smith)

Stat 700, Mathematical Statistics I

Stat 430, SAS and Regression

RIT on Survival Analysis (no web-page)

Stat 705, Computational Statistics

Stat 798L, Survival Analysis

RIT on Estimating Equations

** Past Teaching: Mini-Courses**

**Mini-Course on Cross-Classified Factor Analysis**

**Lecture** (10/17/05) **Mathematical Challenges in Cross-ClassifiedFactor Analysis**

Summary of interesting mathematical issues related to PhD theses about Factor Analysis

by my former students Yang Cheng(2004) and Sophie (Hsiao-Hui) Tsou (2005).

**Mini-Course on Markov Chain Monte Carlo**

(Statistical Simulation Techniques) Spring '04: 4/21, 4/28

Slides can be found at links indicated under eachlecture below.

The lecture topics are as follows:

**Lecture1** (4/21/04) Metropolis Hastings Algorithm -- Motivation from Accept-Reject

simulation methodology and from MarkovChain theory. Extended example and issues

involved in the choice of `proposalMarkov chain' from which the Metropolis-Hastings

chain is built. Gibbs Sampler motivation.**Lecture2** (4/28) Recap of Gibbs Samplermotivation. Testing for Markov Chain Monte Carlo

convergence from the internal evidenceof the Gibbs Sampler trajectory. Statistical examples:

Bayesian statistical computation andfrequentist treatment of hierarchical statistical models.

**Mini-Course on Statistics of Survival Data **(Fall '02: 11/6, 11/13, 11/20)

Slides can be found at links indicated under eachlecture 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) Survival-data likelihoods with Infinite-Dimensional Parameters

(1) ** Fall '05 **

(2) **Spring '04 RIT on Meta-Analysis.** Click here for web-page.

Briefly, **meta-analysis** concerns the simultaneous statistical analysisof a number of

related studies or datasets within a single statistical model. The factthat parameters are

shared across datasets (e.g. a treatment-effectiveness parameter assumedconstant

across a number of separately conducted clinical studies of the effectiveness of the same

treatment regimen for the same disease) allows the possiblity of increasingsensitivity or

power of statistical tests. However, such an increase in precision comesat the price of

simultaneous model assumptions whose compatibility with the data must bevalidated.* This RIT was an outgrowth of the Fall '03 RIT on Large Cross-ClassifiedDatasets (see web-page linked below for details).*

(3)

see RITF03web-page .

(4)

In Fall 2002, I ran a `research interaction' seminar including my own graduate advisees

and others, on the mathematical & statisticaltopics which more broadly correspond to

the overlapof my students' thesis projects and most of my own current research interests,

namely

problems in which there is a large sample-size n, but where the predictor variables

and/or cross-classifications of the sample units become more complicated or numerous

as n gets large. Such problems range from

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)

In Spring 2002, following up on the Fall 2001 seminar described below, I ran an intensive

seminar onstatistical analysis of DNA Microrarrays, for students consideringresearch

in thisarea. Data-analysis figured prominently, performed by me andalso by two of the

several graduate students who participated.

(6) Genomics/MicroarraySeminar

Mathematical Topics in Functional Genomics. Clickherefor the reading list.

__Other Past Teaching and Seminars __

(1) ** Spring '04,** introductory course **Stat 470** on Actuarial Mathematics, taught primarily

from booknotes which I wrote. Coverage includes theory of interest, life tables,review of

probabilitytheory, expectations of time-discounted insurance costs and premiums

calculatedfrom life tables, and special models of mortality. *Seethe old course web-page (from which the main text can be downloaded a chapter at a time) for furtherdetails.*

(2) ** Spring '04** Stat 798C, **Computational Methods in Statistics**, a graduateintroduction

to statistical computingwith emphasis on the Splus (or R) and SAS computer packages.

(I also taught thiscourse 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 small-area 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 areaincludes small-area and MSE estimation from survey data satisfying nonlinearly transformed Fay-Herriot models or left-censored Fay-Herriot 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 nonresponse-adjustment and calibration of weights in complex surveys can be found in a Census SRD Technical Report.

(II) Survival data analysis, which includes both semiparametricinference 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 decision-theoretic optimalearly-stopping 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) Large-scale data problems with emphasis on cross-classified data, Principal Components (paper on representation of tongue surface duringspeech, 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 high-dimensional Markov processes applied to equilibria in Economics (paper in *Journal of Economic Theory*, for which 2nd pdf file in directory contains Figure); to Protein-folding; and to ascertainment of number of distinct DNA `species' from sequencing experiments.