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 2017
Stat 730, Time Series

    Fall 2016
Stat 705, Statistical Computing in R

    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 Equations in Surveys and Biostatistics

         Last few years' teaching

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

    Fall 2010
RIT on Biased Sampling

    Fall '09
RIT on Multilevel Statistical Models (joint with P. Smith)

    Spring '09
RIT on Semiparametric Statistics

    Fall '08
RIT on Survival Analysis (no web-page)

    Spring '08
RIT on Estimating Equations

                  Past Teaching: Mini-Courses

Mini-Course on Cross-Classified Factor Analysis

Lecture   (10/17/05) Mathematical Challenges in Cross-Classified Factor 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 each lecture below.
    The lecture topics are as follows:

Lecture 1   (4/21/04) Metropolis Hastings Algorithm -- Motivation from Accept-Reject
      simulation methodology and from Markov Chain theory. Extended example and issues
      involved in the choice of `proposal Markov chain' from which the Metropolis-Hastings
      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.

Mini-Course 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)  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 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 treatment-effectiveness 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 Cross-Classified Datasets
        (see web-page linked below for details).

            (3)   Spring and Fall '03 RIT on Statistics of Large Cross-Classified Datasets:
        see RITF03 web-page .

            (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 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 Semiparametric Statistical Inference  to
        Order-selection 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.   Data-analysis 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 time-discounted  insurance costs and premiums
        calculated from life tables, and special models of mortality. See the old course web-page
        (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


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 a comparative SAE study about SAIPE that I co-authored. My methodological research in this area includes small-area and MSE estimation from survey data satisfying nonlinearly transformed Fay-Herriot models or left-censored Fay-Herriot models. A Discussion of a Review Paper of JNK Rao on Small Area Estimation mentions several research directions in this problem area that are still highly relevant.
       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 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 work relates to decision-theoretic optimal early-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.
A relatively recent paper I wrote with a student describes models arising in survival analysis, but also in reliability and other fields, allowing development of flexible families of parametric densities for survival times.

(III) Large-scale data problems with emphasis on cross-classified 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 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, April 5, 2017.