Latest revision as of 06:38, 13 October 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 Expected Value of MLE estimate over standard deviation and expected deviation

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 <center><font size= 4>
 Questions and Comments for:
-'''[[Neyman-Pearson_Lemma_and_Receiver_Operating_Characteristic_Curve|Neyman-Pearson Lemma and Receiver Operating Characteristic Curve]]'''
+'''[[ECE662Selecture_zhenpengMLE|Expected Value of MLE estimate over standard deviation and expected deviation]]'''
 </font size>
-A [https://www.projectrhea.org/learning/slectures.php slecture] by Soonam Lee
+A [https://www.projectrhea.org/learning/slectures.php slecture] by Zhenpeng Zhao
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 =Questions and Comments=
-Review by Soonam Lee:
-# This slecture mainly talks about curse of dimensionality using easy metaphor. Assume that our goal is finding the needle in N-D haystack. If the haystack lies in 1-D, simple search followed by one axis or basis is enough. However, as number of dimension increases, the number of basis increases and it produces exponentially expensive search problem. The author explains this concept with various pictures. Moreover, this slecture describes sparsity of samples that is from increasing dimension. With the Gaussian reconstruction using different number of samples, the author conveys the idea efficiently. Lastly, three ways are introduced to break this curse of dimensionality such as feature extraction method, dimensionality reduction techniques, and kernel method. Firstly, feature extraction method is the one way that selects and extracts meaningful features from given redundant information. On the other hands, dimensionality reduction technique gave us the way to project from the high dimensional space to low dimensional space. In this case, how to choose the project axes are very important tasks. PCA uses the largest variance axes as a projection axes whereas LDA decides these axes based on the best separation across class. Apart from these two methods, kernel methods maps data to much higher dimensions but the data should be explained with high dimension.
+If any one have already reserved this selecture for a review please send me an email @ s-fang@purdue.edu
-# Overall, the contents are very simple and intuitive. More specifically, the metaphor which describes the curse of dimensionality is easy to understand. The author uses several pictures and it helps even beginner to understand this concept. Also, the sparsity caused by increasing dimension is also clear because the slecture explains the concept with pictures. Lastly, dimensional reduction technique is also easily understood.
-# I have some suggestions to make this slecture more abundant. First, the author uses the word overfitting which is the lack of ability to reliably estimate and generalize. However, overfitting is not coming from lack of training data. Perhaps, underfitting is the appropriate word that have bad estimation performance due to sparse samples. The definition of overfitting is such that it fits very well for training data, but it doesn't fit well for testing data. It means since it captures unnecessary details such as noise from training data, it did not fit well in testing data. Second, since PCA and LDA are not difficult idea, he can put another pictures to explain them easier. The last figures can help understanding dimensionality reduction technique roughly, but did not provide information about PCA or LDA itself. Since we have several nice slecture to explain this, I will link them as below.
+Reviewing by Shaobo Fang (to be continued):
-* '''[[PCA|Principal Component Analysis (PCA)]]'''
+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 '''[[Neyman-Pearson_Lemma_and_Receiver_Operating_Characteristic_Curve|Neyman-Pearson Lemma and Receiver Operating Characteristic Curve]]'''
+Back to '''[[ECE662Selecture_zhenpengMLE|Expected Value of MLE estimate over standard deviation and expected deviation]]'''

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Latest revision as of 06:38, 13 October 2014

Questions and Comments

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