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.
