Parsimony in statistical modeling is often discussed in terms of Occam’s razor in the formulation of hypotheses. Address the following:
- Discuss the issues of overfitting versus using parsimony and how this is particularly important in big data analysis.
- Is overfitting more of a problem in the generalized least squares model?
- Discuss some hierarchical methods that do not require the parsimony of generalized least squares model.
- Discuss these hierarchical methods, and provide links to any references that you find on the topic.
Be substantive and clear, and use scholarly examples to reinforce your ideas.