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=Questions and Comments= | =Questions and Comments= | ||
| + | If any one have already reserved this selecture for a review please send me an email @ s-fang@purdue.edu | ||
| + | |||
| + | Reviewing by Shaobo Fang (to be continued): | ||
| + | |||
| + | |||
| + | Comments: First of all, the format needs some work. I have noticed the page number between the lines. | ||
| + | |||
| + | Summary:The author investigated briefly over the expected value of MLE estimate based on standard deviation and expected deviation. The case of maximum likelihood estimation examples for Gaussian R.V. both mu and sigma unknown was investigated and is truely interesting since in real world even if the data come in with Gaussian distribution the parameter is probably still unknown. Biasness of an estimator was also briefly investigaed at the very end. | ||
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| + | Good: The mathmatical derivation is clear and thourough. | ||
| + | |||
| + | Could have been improved: It would be better for the reader if more context would be there to provide better transition regarding different parts. | ||
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Back to '''[[Slecture_template_ECE662S14|TITLE OF YOUR SLECTURE]]''' | Back to '''[[Slecture_template_ECE662S14|TITLE OF YOUR SLECTURE]]''' | ||
Revision as of 08:57, 3 May 2014
Questions and Comments for: Expected Value of MLE estimate over standard deviation and expected deviation
A slecture by Zhenpeng Zhao
Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.
Questions and Comments
If any one have already reserved this selecture for a review please send me an email @ s-fang@purdue.edu
Reviewing by Shaobo Fang (to be continued):
Comments: First of all, the format needs some work. I have noticed the page number between the lines.
Summary:The author investigated briefly over the expected value of MLE estimate based on standard deviation and expected deviation. The case of maximum likelihood estimation examples for Gaussian R.V. both mu and sigma unknown was investigated and is truely interesting since in real world even if the data come in with Gaussian distribution the parameter is probably still unknown. Biasness of an estimator was also briefly investigaed at the very end.
Good: The mathmatical derivation is clear and thourough.
Could have been improved: It would be better for the reader if more context would be there to provide better transition regarding different parts.
Back to TITLE OF YOUR SLECTURE
