This page summarizes my graduate coursework at Iowa State University.

Spring 2015

STAT 503. Exploratory Methods and Data Mining.

Dianne Cook. 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.

Stephen Vardeman. 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 learning.

Fall 2014

STAT 643. Advanced Theory of Statistical Inference.

Huaqing Wu. 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.

Vivekananda Roy. 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.

Spring 2014

GR ST 586. Preparing Future Faculty Intermediate Seminar.

Karen Bovenmyer and Holly Bender. 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.

Karen Bovenmyer and Holly Bender. 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.

Mark Kaiser. 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.

Dan Nordman. 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.

Fall 2013

COM S 511. Design and Analysis of Algorithms.

Giora Slutzki. 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 approximation algorithms.

GR ST 585. Preparing Future Faculty Introductory Seminar.

Karen Bovenmyer and Holly Bender. 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.

Mark Kaiser. 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.

Arka Ghosh. 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.

Spring 2013

STAT 516. Statistical Design and Analysis of Gene Expression Experiments.

Long Qu. 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 statistics.

STAT 544. Bayesian Statistics.

Jarad Niemi. 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.

Ranjan Maitra. 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.

Fall 2012

STAT 515. Theory and Applications of Nonlinear Models.

Derrick Rollins. 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.

Heike Hofmann. 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.

Spring 2012

STAT 511. Statistical Methods.

Dan Nettleton. 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.

Vivekananda Roy. 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 methods, bootstrap.

STAT 585X. Data Technologies in Statistics.

Dianne Cook and Heike Hofmann. 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.

Fall 2011

STAT 500: Statistical Methods.

Alicia Carriquiry. 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.

Song Chen. 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.

Ranjan Maitra. 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.