UMD’s PhD in Statistics provides advanced training in probability, statistical inference, Bayesian methods, and high-dimensional data analysis. Students work on theory, computation, and application across healthcare, economics, environmental science, and more.
Development of Bayesian models for rare disease diagnosis
High-dimensional inference techniques for genomic data analysis
Time series modeling of infectious disease transmission dynamics
Simulation-based methods for causal inference in observational studies
Design of robust statistical learning algorithms for big data
Bayesian model averaging for economic forecasting
Hierarchical modeling of spatial environmental pollutant distributions
Application of bootstrapping to machine learning model validation
Multilevel models for education outcomes across school districts
Dissertation on nonparametric regression techniques
Longitudinal analysis of patient health data using mixed effects models
Survival analysis methods for cancer treatment effectiveness studies
Markov Chain Monte Carlo improvements for complex models
Joint modeling of longitudinal and survival data in clinical research
Small area estimation for government survey data reporting
Multivariate imputation techniques for missing health survey data
Change-point detection in climate time series datasets
Empirical Bayes methods in meta-analysis for public health
Variance reduction techniques in simulation-based statistical methods
Adaptive sampling design for rare population studies
Shape scientific and policy decisions through advanced statistical modeling in UMD’s Statistics PhD.
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