Eric V. Slud

Professor, Statistics Program
Department of Mathematics

University of Maryland
College Park, MD 20742
EVS, Stat
Research Interests

Info on Older RIT's


Contact Information

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

                Current Teaching
        Links to Current Course & RIT Web-Pages

    Spring 2016
Stat 650, Applied Stochastic Processes

    Fall 2015
Stat 705, Statistical Computing in R

    Spring 2015
Stat 701, Mathematical Statistics II

    Fall 2014
Stat 700, Mathematical Statistics I

    Spring 2014
Stat 701, Mathematical Statistics II
RIT on Weighted Estimating Equations in Surveys and Biostatistics

    Fall 2013
Stat 700, Mathematical Statistics I
RIT on Weighted Estimating Equationsin Surveys and Biostatistics

    Spring 2011
Stat 401, Applied Prob. and Statistics II
Stat 710, Advanced Statistics -- Large-Sample Theory
Math 420, Mathematical Modeling

    Fall 2010
Stat 401, Applied Prob. and Statistics II
Stat 440, Sampling Theory
RIT on BiasedSampling

         Last few years' teaching

    Spring 2010
Stat 701, Mathematical Statistics II

    Fall '09
Stat 700, Mathematical Statistics I
Stat 705, Computational Statistics (in R)

RIT onMultilevel Statistical Models (joint with P. Smith)

    Spring '09
Stat 701, Mathematical Statistics II
Stat 430, SAS and Regression

RIT onSemiparametric Statistics
AMSC 762 DataAnalysis Seminar (joint with Paul Smith)

    Fall '08
Stat 700, Mathematical Statistics I
Stat 430, SAS and Regression

RIT on Survival Analysis (no web-page)

    Spring '08
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

                Past Research Interaction (RIT) Seminars

           (1) Fall '05    RIT on Statistics of Models with Growing Parameter Dimension

           (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)   Spring and Fall '03 RIT on Statistics of Large Cross-Classified Datasets:
       see RITF03web-page .

           (4)  Intensive Seminar, Fall 2002.  See planfor details.
        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 Statistics of Large Cross-Classified Datasets. Roughly speaking, these are
        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 SemiparametricStatistical Inference  to
        Order-selection problems in regression and time series, to Classificationand 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 planfor details.
        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    Fall 2001,    AMSC 699:
        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


My primary research interests are in mathematical statistics and probability, specifically in the following areas:

(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.


The Campus Statistics Consortium page.

The UMCP Statistics Program home page.

The UMCP Math Department home page.

The University of Maryland home page.

© Eric V Slud, January 27, 2016.