Question

R1. Now that you've seen both MME and MLE, we might begin comparing the two worlds. In class, we studied XUnif(0,0) and showed the MLE is OSILE-max Xi. One can show the MME for this setup is OMME -2 X. As seen in HW2, these estimators are RVs, and each will have its own (sampling) distribution. The sampling distribution gives a good sense of what types of values you'll get from θ when you draw a random sample. Use the internet to learn how to create overlapping histograms in R. Then, on the same set of axes, make a red histogram that shows 10000 values of θΜΜΕ based on 10000 random samples of size 40. Next, overlay a green histogram that shows 10000 values of θΜ LE based on 10000 new randon sarnples of size 40, Draw from the Unif(0,6) distribution. Include your code and a sketch of the overlapping histograms R2. List a strength and a weakness of IME based on what you see. Then, list a strength and a weakness of MLE

Answer 1