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 May 19, 2017
 Introduction to Statistical Machine Learning and Its Applications:
Yufeng Liu
 Statistical machine learning is an interdisciplinary
research area which is closely related to statistics,
computer science, engineering, and bioinformatics.
Many statistical machine learning techniques and
algorithms are very useful for various scientific areas.
This full day short course will provide an overview of
statistical machine learning techniques with
applications to the analysis of big biomedical data.
 May 13, 2016
 Working With Categorical Variables:
Loglinear Model, Association Graph, and Multigraph:
Harry J. Khamis
 This course will provide the attendee with the statistical
tools needed to glean information from categorical
data accurately, and to avoid the pitfalls that can
occur when working with such data. The course is based on the monograph: The
Association Graph and the Multigraph for Loglinear
Models (SAGE Publications, Inc., 2011) by Harry J.
Khamis.
 May 8, 2015
 Applied Logistic Regression:
Rodney X. Sturdivant
 The aim of this course is to provide theoretical and
practical training for biostatisticians and professionals
of related disciplines in statistical modeling using
logistic regression. The increasingly popular logistic
regression model has become the standard method
for regression analysis of binary data in the health
sciences.
 April 11, 2014
 Bayesian Methods and Computing for
Data Analysis and Adaptive
Clinical Trials:
Bradley P. Carlin
 This oneday short course introduces Bayesian
methods, computing, and software, and goes on to
elucidate their use in Phase I and II clinical trials, as
well as metaanalysis of current and historical trials.
We include descriptions and live demonstrations of
how the methods can be implemented in BUGS, R,
and versions of the BUGS package callable from
within R.
 May 31, 2013
 An Arboretum of Graphics:
Marepalli B. Rao
 The main
objective of this workshop is to empower you to
produce graphs, which could highlight salient features
of your research. The medium we will use is the
statistical software R, which is free. This software is
making waves in the realm of data analysis and
graphics and one has absolute control how one wants
to mold and present one's graphics. The instructor and graduate students will guide you
through the graphical maneuvers. A vast array of
examples coming from a variety of research areas,
including health sciences and industry, will be used
for illustration.
 May 11, 2012
 Recursive Partitioning and Applications:
Heping Zhang
 This seminar will cover nonparametric
regression methods built on recursive
partitioning, a statistical technique that forms the
basis for two classes of nonparametric
regression methods: Classification and
Regression Trees (CART) and Multivariate
Adaptive Regression Splines (MARS). Although
relatively new, the applications of these methods
are far reaching, as study designs from the arts,
engineering, science and commercial
applications become increasingly complex,
yielding data sets of massive size.
This seminar covers Chapters 1 to 4, 6, 8, and 9
from the book entitled "Recursive Partitioning
and Applications" authored by Heping Zhang
and Burton Singer and published by Springer in
2010.
 May 16, 2011
 Pictures at an Exhibition:The Visual Display of Quantitative Phenomena:
Howard Wainer
 This oneday seminar explores the value of graphical display:
 How to Display Data Badly
 Understanding Tables
 Mutivariate Display
 Pictures at an Exhibition:
Sixteen Visual Conversations About One Thing
 The Most Dangerous Equation
The seminar is designed for anyone who must either convey or receive quantitative information.
 May 14, 2010
 An Introduction to Some Multivariate Methods:
Dallas E. Johnson
 Many common uses of multivariate statistical methodologies have
similar roots. This seminar will provide an overview of multivariate methods,
including data matrices and vectors, estimates of mean vectors and sample covariance and
correlation matrices.
It will introduce several of the most popular and useful techniques including principal
component analysis and factor analysis. Discriminant analysis will also be covered using both
traditional discriminant methods and logistic regression.
Examples and statistical computing software will be used to illustrate the techniques considered.
 May 15, 2009
 Applied Mixed Models:
Linda J. Young
 Data sets from designed experiments, sample surveys,
and observational studies often contam correlated observations due to random effects
and repeated measures. Mixed models can be used to accommodate the correlation structure,
produce efficient estimates of means and differences between means, and provide valid
estimates of standard errors.
Normal theory models for random effects and repeated measures ANOVA will be used to
intoduce the concept of correlated data. These models are then extended to generalized
linear mixed models for the analysis of nonnormal data, including binomial responses,
Poisson counts, and overdispered count data. Methods for assessing the fit and dedciding
among competing models will be discussed.
Accounting for spatial correlation and radial smoothing splines within mixed models
will be presesnted and their application illustrated. The use of SAS System's PROC GLIMMIX
will be introduced as an extension of PROC MIXED and used to analyze data from pharmaceutical
trials, environmental studies, educational research, and laboratory experiments.
 May 9, 2008
 Applied Longitudinal Analysis:
Garrett Fitzmaurice
 The goal of this seminar is to provide an introduction to
statistical methods for analyzing longitudinal data. The main emphasis is on the practical
rather than the theoretical aspects of longitudinal analysis.
The course begins with a review of established methods for analyzing longitudinal
data when the response of interest is continuous.
A general introduction to linear mixed effects models for continuous responses is presented.
When the response of interest is categorical (e.g., binary or count data), a number of
extensions of generalized linear models to longitudinal data have been proposed.
We present a broad overview of two main types of models: "marginal models"
and "generalized linear mixed models". While both classes of models account for the
withinsubject correlation among the repeated measures, they differ in approach.
Moreover, these two classes of models have regression coefficients with quite distinct
interpretations and address somewhat different questions regarding longitudinal change
in the response. In this course we highlight the main distinctions between these two
types of models and discuss the types of scientific questions addressed by each.
 May 21, 2007
 Regression Modeling Strategies:
Frank E. Harrell, Jr.
 The first part of this course presents the following elements of multivariable
predictive modeling for a single response variable: using regression splines to relax linearity assumptions, perils of
variable selection and overfitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction,
and interaction surfaces. Then a default overall modeling strategy will be described. This is followed by methods for graphically
understanding models (e.g., using nomograms) and using resampling to estimate a model's likely performance on new data.
Then the freely available SPlus Design library will be overviewed. Design facilitates most of the steps of the modeling process.
Two of the following three case studies will be presented: an interactive exploration of the survival status of Titanic passengers,
an interactive case study in developing a survival time model for critically ill patients, and a case study in Cox regression.
The methods in this course will apply to almost any regression model, including ordinary least squares,
logistic regression models, and survival models.
 May 15, 2006
 Causal Inference in Experiments and Observational Studies:
Donald B. Rubin
 This course will present the Rubin Causal Model perspective
for statistical inference for causal effects through potential outcomes. There are three parts to the course.
The first part establishes the primitives that form the foundation: units, treatments, potential outcomes,
the stability assumption, and the assignment mechanism. The second part presents inference based solely on
the assignment mechanism; this perspective generalizes Fisher's (1925) and
Neyman's (1923) randomizationbased approaches, and emphasizes the central role of the
propensity score (Rosenbaum and Rubin, 1983), thereby creating a bridge between experiments and
observational studies. The third part presents inference based on predictive models for the distribution
of the missing potential outcomes, formally, Bayesian posterior predictive inference (Rubin, 1978).
In practice, the predictive approach is ideal for creating statistical procedures, whereas the
assignmentbased approach of Fisher is ideal for traditional confirmatory inference, and the
assignmentbased approach of Neyman is ideal for evaluating procedures.
For best practice, being facile with all three approaches is important.
 April 1, 2005
 Survival Analysis: Statistical Methods for Censored and Truncated Data:
Melvin L. Moeschberger
 In this tutorial session,
basic elements of survival analyses will be presented. The quantities used to
summarize event time data will be defined. The speaker will focus on nonparametric
estimation of these quantities including the KaplanMeier estimator. Weighted log
rank tests and their use in comparing the survival experience in two or more populations
will be discussed. Focus will be on regression techniques for censored data based
on the Cox regression model. Testing and model building using fixed and time dependent
covariates through a series of examples will be examined. The material covered in the
session will be taken from the new second edition of the speaker's book. Methods will
be illustrated using medical data and SAS statements will be discussed to perform various
analyses.
 April 26, 2004
 Categorical Data Analysis:
Alan Agresti
 This tutorial surveys methods for correlated
categorical data, which occur with repeated measurement and other forms of
clustering. The main focus is on two types of models. One type models marginal
distributions, e.g., with generalized estimating equation (GEE) methodology.
The other type uses random effects to describe subjectspecific conditional
distributions. Emphasis is on logit models for binary responses, but with some
discussion of ordinal responses. Examples use SAS, mainly PROC GENMOD and
NLMIXED.
 May 19, 2003
 An Introduction to Bayesian Modeling:
James Albert
 This seminar presents an overview of Bayesian modeling, emphasizing the rationale
behind Bayesian thinking, rather than the technical aspects of modeling. The basic
elements of Bayesian analysis, including choice of a prior, likelihood, and
summarization of the posterior distribution will be illustrated in the simple
setting of learning about a population proportion. Bayesian models will be surveyed
for fitting continuousresponse data, including regression and ANOVA models. Generalized
linear models, such as logistic regression and itemresponse models will also be discussed.
Examples of the use of Bayesian software will be provided. In all sessions, there will be
a comparison of classical and Bayesian methods, with guidelines on the appropriateness
of each method.
 May 6, 2002
 Data Mining: Where Do We Go From Here?:
Dick De Veaux
 The sheer volume and complexity of data collected or available to
most organizations have propelled data mining to the forefront of
making profitable and effective use of data.
In this course, we'll take a brief tour of the current state of
data mining algorithms. Using several case studies, we will examine how
exploratory data modeling can be used to narrow the search for a
predictive model and to
increase the chances of producing useful and meaningful results.
 May 24, 2001
 Robust Engineering Using Taguchi
Methods: Shin Taguchi
 This seminar will address the following topics:
 Why robust design?
 How to apply Robust Design upstream
 How to define robustness
 Identify ways to measure robustness
 Determine applicability of Robust Design to your product of
process
 How to determine the Ideal Function of a system
 How to translate customer intent and perceived result into
engineering terms (signal and output response)
 How to identify noise and control factors of the system
 Define and differentiate between noise and control factors
 Four strategies to address the effects of noise on the system
 How to select test levels for noise and control factors
 How to lay out, conduct, analyze, evaluate, and confirm tests
and results
 Calculate SignaltoNoise Ratio and use it effectively for
data analysis
 Identify control factors that show a high SignaltoNoise
Ratio
 Evaluate and improve robustness using the SignaltoNoise
Ratio
 Determine optimum control factor levels while maintaining or
reducing cost
 May 15, 2000
 From Shewart Monitoring to BoxJenkins
Adjustments: J. Stuart Hunter
To hit a production target with least variability requires both
attentive monitoring and careful adjustment. Fully automatic
manufacturing processes are indeed possible, but the role of human
attendants is far from vanishing. This seminar employs models
descriptive of the industrial environment appropriate to the
Shewhart monitoring chart and to the new BoxJenkins adjustment
chart. In application both charts are informative graphics
designed for handson realtime use on the production floor. The
two charts contribute to the full practice of SPC, "Statistical
Process Control".
The BoxJenkins model for a production process is that of a
wandering mean about a fixed target. A simple statistic for such
autocorrelated data is the EWMA, the Exponentially Weighted Moving
Average. The EWMA can be employed as a smoother of a noisy
wandering time series, and as an estimate of the current level of
the series. When an estimated process level departs importantly
from target an adjustment is usually ordered. The BoxJenkins
Manual Adjustment Chart provides both the estimated process level
and the required adjustment. Minimum mean square error about
target is the primary objective. In operation the BoxJenkins
Manual Adjustment Chart procedure is identical to that of
automatic integral feedback control.
 May 10, 1999
 INTRODUCTION TO MIXED MODELS FOR THE
PRACTICING STATISTICIAN  With Analysis Using PROC MIXED of the SAS®
System: George Milliken
Mixed Models are needed to provide the
analysis of data from most designed experiments. The presentation
starts with an introduction to the terminology used in the
discussion of mixed models including definitions of random
effects, fixed effects, random effects models, fixed effects
models and mixed effects models. The oneway analysis of variance
model with unequal variances is used as the starting point of the
discussion of the mixed models. The syntax of PROC MIXED of the
SAS® system is described and the remaining examples are
analyzed with the purposes of (1) providing an understanding of
the features of PROC MIXED and (2) of developing an understanding
of the characteristics of various designs and their analyses. The
splitplot and stripplot designs are used in many settings,
including experiments that are conducted over two or more
processing steps. Examples of industrial applications are used to
demonstrate these designs and their analyses. Repeated measures
designs are the most complex in that there is a correlation
structure among the measurements made on the same experimental
unit. PROC MIXED enables the analysts to model the covariance
structure of the repeated measures and allows for the comparison
of the fits in order to select an appropriate structure. An
example from the pharmaceutical industry is used to demonstrate
the procedures for selecting an appropriate model for a repeated
measures data set. The concepts of narrow, intermediate, and broad
inference spaces are described and their implications are
demonstrated using an example from a multiple location trial, an
experiment where the same design is conducted at several
locations. Finally, the power of PROC MIXED is used to provide the
analyses of an unconnected design and of a crossover design. Each
participant will be provided with a set of notes that consists of
copies of the transparencies used during the presentation. The
goal of the seminar is to provide that participants with tools
that will enable them to identify when a mixed model is needed to
describe a given data set and how to accomplish the analysis using
PROC MIXED of the SAS® system.
 May 4, 1998
 ReInventing Regression thru Graphics:
R. Dennis Cook and Sanford Weisberg
Regression is the study of the change
in a response variable as one or more predictors are varied. It is
used to judge the effectiveness of a treatment, to form prediction
equations, and for many other purposes. We present a new context
for regression that requires few scopelimiting assumptions, and a
corresponding collection of new methodological tools. Many of
these tools use simple graphs, along with a welldeveloped theory,
to discover information about the dependence of a response on the
predictors. All the methods flow from a few key ideas concerning
dimension reduction, understanding the role of the distribution of
the predictors, and thinking about and using graphs in regression
analysis.
Regression graphics have developed rapidly over
the past six years, and many new developments are in progress. The
topics were selected to be immediately useful in applications, to
set the stage for future study in the area and show its promise.
Regression analysis is one of the fundamental
tools for the practicing statistician. The traditional role of
graphics in regression, at least with many predictors, has been
peripheral, used mostly to judge model adequacy. Regression
graphics moves graphs to the center of analysis. The new theory
will be presented at a very general and intuitive level. We want
to encourage to participants to use this approach in their own
work and teaching.
The methodology described is very general, and
can be used in almost any regression problem. in the course of the
workshop, we will work examples with both continuous and binary
responses.
All the methodology discussed will be
illustrated with a computer package (Rcode) that can be used for
all the new methods described, and many standard methods for
linear regression, nonlinear regression and generalized linear
models. A copy of the most recent version of Rcode, which runs on
the Mac, PC, or Unix, will be made available to all workshop
participants for use in their own work.
 May 19, 1997
 Artificial Neural Networks: A
Practical Introduction: Richard D. De Veaux. and Lyle H. Ungar
Artificial neural networks are being used with
increasing frequency for prediction and classification in high
dimensional problems. Due to a variety of reasons, not the least
being that they were originally inspired by attempts to model the
human brain, they have received enormous publicity. This has led
to a variety of claims by users of artificial neural networks and
a great deal of misinformation about what these models can and
cannot do well. Often, statisticians have been put into the
position of having to defend their own practices against these
claims.
This course provides a tutorial overview of
artificial neural networks, focusing primarily on the most
commonly used network, the backpropagation network. We explain,
from a statistician's vantage point, why neural networks might be
attractive and how they compare to other statistical estimation
techniques. We will discuss the practical implementation issues
surrounding the use of neural networks in applications. Modern
statistical methods can be used on many of the same problems that
neural networks are used for. We compare the use of neural
networks to these statistical methods and discuss the relative
advantages and trade offs between the use of these different
tools.

 May 20, 1996
 Statistical Intervals: A User's Guide: Gerald J. Hahn and
William Q. Meeker
 June 1995
 Analysis of Messy Data: Dallas E. Johnson, Kansas State
University
 May 1994
 Interfaces Between Statistical Process Control and
Engineering Process Control: John F. MacGregor
 May 1993
 What Do You Do When Standard Designs Don't Fit?:
Christopher J. Nachtsheim, University of Minnesota
 May 1992
 Regression Modeling: Separating Signal from Noise in Data:
Richard F. Gunst
 May 1991
 Design of Experiments: Douglas C. Montgomery
 May 1990
 Response Surface Techniques and Mixture Experimentation:
John A. Cornell
 May 1989
 Statistical Methods for Quality and Productivity Improvement:
George E. P. Box and R. Daniel Meyer, The Lubrizol Corporation
 May 1988
 Control Charting  Beyond Shewhart: J. Stuart Hunter
 May 1987
 Regaining the Competitive Edge: Howard E. Butler, Harold
S. Haller, Robert V. Hogg, J. Stuart Hunter, R. Daniel Meyer, and
Robert N. Rodriguez
