Methodological Projects
You will choose one area to work in. Note that these are likely new-to-you topics. You will first learn about the topic and demonstrate how to apply the new methodology to a real dataset. The final deliverables from the project will be a written paper and an oral presentation with slides.
The methodologies to consider:
- Missing Data and Imputation
- Long Short-Term Network
- Quantile Regression
- Generalized Additive Models
- Normal Linear Mixed Models
- LASSO and Ridge Regression
- Cross-validation
- Bootstrapping and Jackknife Estimation
- Kernel Regression
- K-means clustering
- Principal Component Analysis
- Generalized estimating equations
- Cox proportional hazards model
- Support Vector Machines
- Random Forest
- Bayesian Networks
- Latent Class Analysis
- Bayesian Linear Regression
- Beta regression
- etc.
You are free to propose a topic if there is something you are interested in but is missing from the list. The instructor must approve the methodology to ensure that it meets the expectations of a capstone project.
Useful links
- Data Sets: https://acohenstat.github.io/Datasets/
- GitHub Page for the project: https://github.com/acohenstat/STA6257_Project