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ECE 695A Lecture 33R: Review Questions

By Muhammad Alam

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Abstract

Review Questions:

  1. With higher number of model parameters, you can always get a good fit – why should you minimize the number of parameters
  2. Least square method is a subset of maximum likelihood approach to data fitting. Is this statement correct?
  3. What aspect of the distribution function does Cox-Oakes method emphasize?
  4. Can MLE be used for any distribution function?
  5. How would you change the MLE condition if you had 3 independent parameters to estimate?
  6. Does increase in model parameters increase chances of passing c2 test?
  7. How does the methods affected by censored data (e.g. , TDDB test yet to finish?)

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Researchers should cite this work as follows:

  • Muhammad Alam (2013), "ECE 695A Lecture 33R: Review Questions," http://nanohub.org/resources/17616.

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Location

EE 226, Purdue University, West Lafayette, IN

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