- With higher number of model parameters, you can always get a good fit – why should you minimize the number of parameters
- Least square method is a subset of maximum likelihood approach to data fitting. Is this statement correct?
- What aspect of the distribution function does Cox-Oakes method emphasize?
- Can MLE be used for any distribution function?
- How would you change the MLE condition if you had 3 independent parameters to estimate?
- Does increase in model parameters increase chances of passing c2 test?
- How does the methods affected by censored data (e.g. , TDDB test yet to finish?)
Researchers should cite this work as follows:
Muhammad Alam (2013), "ECE 695A Lecture 33R: Review Questions," http://nanohub.org/resources/17616.
EE 226, Purdue University, West Lafayette, IN