The PhD in Statistics at the University of Wisconsin–Madison prepares students for research and leadership roles in academia, industry, and government. The program emphasizes both theoretical foundations and practical applications, with opportunities in machine learning, biostatistics, econometrics, environmental modeling, and more.
Bayesian Hierarchical Modeling for Small-Area Estimation
Nonparametric Methods for High-Dimensional Data
Causal Inference in Observational Healthcare Studies
Time Series Forecasting Using State Space Models
Robust Statistical Methods for Outlier Detection
Variable Selection in High-Dimensional Genomic Data
Bayesian Networks for Risk Prediction in Finance
Statistical Approaches for Missing Data Imputation
Design and Analysis of Adaptive Clinical Trials
Functional Data Analysis for Wearable Health Sensors
Monte Carlo Methods for Complex Probability Distributions
Spline-Based Regression Techniques for Longitudinal Data
Deep Learning Meets Statistics: Uncertainty Quantification
Graphical Models in Social Network Analysis
Survival Analysis with Competing Risks Framework
Bootstrap Techniques in Model Validation
Spatial Statistics in Environmental Risk Modeling
Statistical Modeling for Election Forecasting
Latent Variable Models for Educational Testing
Interpretable Machine Learning Models in Biostatistics
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