This page summarizes my graduate coursework at
Iowa State University.
STAT 503. Exploratory Methods and Data Mining.
Approaches to finding the unexpected in data; pattern recognition,
classification, association rules, graphical methods, classical
and computer-intensive statistical techniques, and problem solving.
Emphasis is on data-centered, non-inferential statistics for
large or high-dimensional data, topical problems, and building
report writing skills.
STAT 602. Modern Multivariate Statistical Learning.
This course will cover statistical theory and methods for
modern data mining, inference, and prediction. Among the
topics considered will be linear methods of prediction and
classification, basis expansions and regularization, kernel
smoothing methods, variance-bias trade-offs, inference and
model averaging, additive models and trees, boosting, neural
nets, support vector machines, prototype methods, unsupervised
STAT 643. Advanced Theory of Statistical Inference.
Sufficiency and related concepts, completeness, exponential
families and statistical information. Elements of decision theory,
decision rules, invariance and Bayes rule. Maximum likelihood
and asymptotic inference. Generalized estimating equations and
estimating functions, M-estimation, U-statistics. Likelihood ratio
tests, simple and composite hypotheses, multiple testing.
Bayesian inference. Nonparametric inference, bootstrap,
empirical likelihood, and tests for nonparametric models.
STAT 644X. Advanced Bayesian Theory.
Exchangeability, reference priors,
improper priors, the Laplace approximation and
other approximations to posterior distributions and
their moments, discrete and general state space
Markov chains, Harris recurrence, Gibbs Sampling,
and the Metropolis-Hastings algorithm.
GR ST 586. Preparing Future Faculty Intermediate Seminar.
Consideration of a wide range of faculty life issues. Includes topics such as
higher education trends, diversity issues, learning styles, assessment,
grant and proposal writing, and legal and ethical issues. Written components
include job and teaching portfolios.
GR ST 587. Preparing Future Faculty Teaching Practicum.
Students complete a stand-alone teaching assignment at Iowa State or another
higher education institution. Written components include pedagogical documents.
STAT 601. Advanced Statistical Methods.
Methods of constructing complex models including adding parameters to existing
structures, incorporating stochastic processes and latent variables. Use of
modified likelihood functions. quasi-likelihoods; profiles. composite likelihoods.
Asymptotic normality as a basis of inference. Godambe information. Sample reuse.
block bootstrap; resampling with dependence. Simulation for model assessment.
Issues in Bayesian analysis.
STAT 642. Advanced Probability Theory.
Probability spaces and random variables. Kolmogorov's consistency theorem.
Independence, Borel-Cantelli lemmas and Kolmogorov's 0-1 Law. Comparing
types of convergence for random variables. Sums of independent random
variables, empirical distributions, weak and strong laws of large numbers.
Convergence in distribution and its characterizations, tightness,
characteristic functions, central limit theorems and Lindeberg-Feller
conditions. Conditional probability and expectation. Discrete parameter
martingales and their properties and applications.
COM S 511. Design and Analysis of Algorithms.
A study of basic algorithm design and analysis techniques.
Advanced data structures, amortized analysis and randomized algorithms.
Applications to sorting, graphs, and geometry. NP-completeness and
GR ST 585. Preparing Future Faculty Introductory Seminar.
Introduction to faculty life issues such as hiring, tenure, teaching,
and service at a variety of higher education institutions. Includes
presentations from faculty at other institutions.
STAT 520. Statistical Methods III.
Nonlinear regression; generalized least squares; asymptotic inference.
Generalized linear models; exponential dispersion families; maximum
likelihood and inference. Designing Monte Carlo studies; bootstrap;
cross-validation. Fundamentals of Bayesian analysis; data models,
priors and posteriors; posterior prediction; credible intervals;
Bayes Factors; types of priors; simulation of posteriors;
introduction to hierarchical models and Markov Chain Monte Carlo methods.
STAT 641. Foundations of Probability Theory.
Sequences and set theory; Lebesgue measure, measurable functions.
Absolute continuity of functions, integrability and the fundamental
theorem of Lebesgue integration. General measure spaces, probability
measure, extension theorem and construction of Lebesgue-Stieljes
measures on Euclidean spaces. Measurable transformations and random
variables, induced measures and probability distributions. General
integration and expectation, Lp-spaces and integral inequalities.
Uniform integrability and absolute,continuity of measures.
Probability densities and the Radon-Nikodym theorem. Product spaces
and Fubini-Tonelli theorems.
STAT 516. Statistical Design and Analysis of Gene Expression Experiments.
Introduction to two-color microarray technology including cDNA and oligo microarrays;
introduction to single-channel platforms (Affymetrix GeneChips); the role of blocking,
randomization, and biological and technical replication in microarray experiments;
design of single-channel and two-color microarray experiments with factorial treatment
structure; normalization methods; methods for identifying differentially expressed
genes including mixed linear model analyses, empirical Bayes analyses, and resampling
based approaches; adjustments for multiple testing; clustering and classification
problems for microarray data; emphasis on current research topics in microarray
STAT 544. Bayesian Statistics.
Specification of probability models; subjective, conjugate, and noninformative
prior distributions; hierarchical models; analytical and computational techniques
for obtaining posterior distributions; model checking, model selection, diagnostics;
comparison of Bayesian and traditional methods.
STAT 580. Statistical Computing.
Introduction to scientific computing for statistics using tools and concepts in R:
programming tools, modern programming methodologies, modularization, design of
statistical algorithms. Introduction to C programming for efficiency; interfacing
R with C. Building statistical libraries. Use of algorithms in modern subroutine
packages, optimization and integration. Implementation of simulation methods;
inversion of probability integral transform, rejection sampling, importance
sampling. Monte Carlo integration.
STAT 515. Theory and Applications of Nonlinear Models.
Construction of nonlinear statistical models; random and systematic model
components, additive error nonlinear regression with constant and
non-constant error variances, generalized linear models,
transform both sides models. Iterative algorithms for
estimation and asymptotic inference. Basic random parameter
models, beta-binomial and gamma-Poisson mixtures. Requires use
of instructor-supplied and student-written R functions.
STAT 557. Statistical Methods for Counts and Proportions.
Statistical methods for analyzing simple random samples when
outcomes are counts or proportions; measures of association
and relative risk, chi-squared tests, loglinear models, logistic
regression and other generalized linear models, tree-based methods.
Extensions to longitudinal studies and complex designs, models
with fixed and random effects. Use of statistical software: SAS,
S-Plus or R.
STAT 511. Statistical Methods.
Introduction to the general theory of linear models, least squares
and maximum likelihood estimation, hypothesis testing, interval
estimation and prediction, analysis of unbalanced designs. Models
with both fixed and random factors. Introduction to non-linear and
generalized linear models, bootstrap estimation, local smoothing
methods. Requires use of R statistical software.
STAT 543: Theory of Probability and Statistics II.
Point estimation including method of moments, maximum likelihood
estimation, exponential family, Bayes estimators, Loss function
and Bayesian optimality, unbiasedness, sufficiency, completeness,
Basu s theorem; Interval estimation including confidence intervals,
prediction intervals, Bayesian interval estimation; Hypothesis
testing including Neyman-Pearson Lemma, uniformly most powerful
tests, likelihood ratio tests; Bayesian tests; Nonparametric
STAT 585X. Data Technologies in Statistics.
Read and combine data from flat files, SQL database,
binary netCDF, and making use of web technologies as
data source. Clean data, check quality, impute missing values.
Write efficient, reproducible code. Develop, debug, profile,
and package software. Experiment with event driven programming
to build an interactive graphic and a GUI. Provide experience
in pulling data together to solve a contemporary problem.
STAT 500: Statistical Methods.
Introduction to methods for analyzing data from experiments
and observational data. Design-based and model-based inference.
Estimation, hypothesis testing, and model assessment for 2
group and k group studies. Experimental design and the use
of pairing/blocking. Analysis of discrete data. Correlation
and regression, prediction, model selection and diagnostics.
Simple mixed models including nested random effects and split
plot experimental designs. Use of the SAS statistical software.
STAT 542: Theory of Probability and Statistics I.
Sample spaces, probability, conditional probability;
Random variables, univariate distributions, expectation,
median, and other characteristics of distributions, moment
generating functions; Joint distributions, conditional
distributions and independence, correlation and covariance;
Probability laws and transformations; Introduction to the
Multivariate Normal distribution; Sampling distributions,
order statistics; Convergence concepts, the law of large
numbers, the central limit theorem and delta method; Basics
of stochastic simulation.
STAT 579: An Introduction to R.
An introduction to the logic of programming, numerical algorithms,
and graphics. The R statistical programming environment will be
used to demonstrate how data can be stored, manipulated, plotted,
and analyzed using both built-in functions and user extensions.
Concepts of modularization, looping, vectorization, conditional
execution, and function construction will be emphasized.