Modeling/Simulation
Numerical methods, maximum likelihood estimation, EM methods, optimization, bootstrap techniques, Markov Chain Monte Carlo techniques, simulation and construction of statistical models, time series models.
Regression Analysis
Definition, fitting, inference, prediction, and interpretation of linear regression models. Variable selection, residual and influential diagnostics, mediation, moderation, conditional effects, transformations, generalized least squares and weighted least squares, derived predictors, non-parametric procedures and modeling, generalized estimating equations (GEE), correlational analysis.
Sampling
Simple random sampling, stratified sampling, cluster sampling, proportional probability sampling, ratio and regression estimates, random response, capture-recapture and jackknife techniques.
Design of Experiments
ANOVA including model assessment, multiple comparison, choice of sample size, nonparametric alternatives, randomized complete block design, Latin Square design, factorial design, random effects models, nested designs, split-plot designs, ANCOVA, repeated measures, and multivariate analysis.
Observational Studies
Cross-sectional, case-control, cohort, prospective or retrospective, propensity score matching.
Survey Design
Development and testing of qualitative and quantitative instrumentation. Validity and reliability testing. Exploratory Factor Analysis, Confirmatory Factor Analysis, Structural Equation Modeling.
Quality Control/Improvements
Pareto analysis, control charts, process capability, acceptance sampling, Taguchi method, parameter design, tolerance design, reliability, hazard rate, censoring, accelerated life testing.
Time Series Analysis
Univariate stationary and non-stationary models, vector autoregressions, ARIMA models, trend analysis, seasonal models, exponential smoothing, Box-Jenkins models, GARCH models, frequency domain methods, survival analysis.
Categorical Data Analysis
Binomial and multinomial variables, chi-square, Fisher’s exact, McNemar’s, and other categorical tests. Logistic regression, multi-category logit models, loglinear models, log-rank tests, Poisson, negative binomial, and other categorical regression techniques.
Statistical Genetics
Large-scale inference, empirical Bayes methods, false discovery rate theory, RNA-seq data analysis, network-based analysis of genomic data, high dimensional regression, genetic networks, causal inference, analysis of microbiome data, ChIP-seq data, epigenomics data.
