Timeline for Comparing two multinomial distributions
Current License: CC BY-SA 4.0
33 events
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| Jun 24, 2020 at 20:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
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| Jan 20, 2020 at 13:51 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
added 2 characters in body
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| Dec 23, 2017 at 9:34 | comment | added | troisquatre | @Todd: Thanks a lot for your suggestion. As for measuring the entropy of a single measure, I'm not a 100% sure I understand what you mean. However, at the moment, I have a full plate which will prevent me from looking into the problem in-depth. Collaborations are welcome though :-) . | |
| Dec 23, 2017 at 9:30 | comment | added | troisquatre | @Martin: Ipparthi D, Winslow A, Sitti M, Dorigo M, Mastrangeli M. Yield prediction in parallel homogeneous assembly. Soft matter. 2017;13(41):7595-608. | |
| Dec 22, 2017 at 3:54 | comment | added | Sextus Empiricus | Could the images from the code be added to this question? And also the full names of the references? Also the process for cluster formation could be explained in the question directly. | |
| Dec 22, 2017 at 0:08 | answer | added | Todd D | timeline score: 1 | |
| Dec 21, 2017 at 14:52 | comment | added | Todd D | A table of observed vs expected values is a good approach. Consider measuring the entropy for a single, albeit mildly abstract, measure. | |
| Dec 20, 2017 at 18:40 | comment | added | troisquatre | @Todd: As I'm looking for how "close" the model gets to the physical experiments, a measure of uncertainty would be useful. In our paper, we present the respective yields and the maximum difference (95% confidence) between the predicted and the experimental absolute yields that we obtain through bootstrapping. | |
| Dec 10, 2017 at 5:39 | comment | added | Todd D | Are you interested in a measure of uncertainty, inference or a descriptive measure? | |
| Jul 2, 2017 at 14:22 | history | tweeted | twitter.com/StackStats/status/881518356750323713 | ||
| Apr 18, 2017 at 9:05 | history | edited | troisquatre | CC BY-SA 3.0 |
Realised the answers to one of my questions, and added a few more questions and comments.
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| Apr 18, 2017 at 8:16 | history | edited | troisquatre | CC BY-SA 3.0 |
Added suggested approach in R
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| Apr 17, 2017 at 14:59 | comment | added | troisquatre | @Chill2Macht : Thanks a lot for the input. If you know someone who might be able to shed light on my problem, please do share my questions with that person. | |
| Apr 16, 2017 at 10:30 | comment | added | Chill2Macht | Well a matrix is a covariance matrix of some distribution if and only if it is positive semi-definite, and every positive semi-definite matrix has a "square root" linear.ups.edu/jsmath/0210/fcla-jsmath-2.10li108.html -- as for how to apply this fact in a statistical context, I have no idea. It would be the blind leading the blind. I wasn't sure if you had thought of the covariance matrix, and sometimes knowing terminology can make accidentally stumbling on the right result on google a lot easier. But other than that I don't really know, I'm sorry. | |
| Apr 16, 2017 at 10:26 | history | edited | Chill2Macht | CC BY-SA 3.0 |
edited tags
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| Apr 15, 2017 at 18:19 | comment | added | troisquatre | @Chill2Macht thanks for your super quick response! I did consider the covariance matrix, but a couple of concerns: (1) I'm not sure what in the covariance matrix I would have to use, and (2) "standard deviation" is (var)^0.5. So, what would that be in a covariance matrix? My grip on stats is not good enough to figure this out by myself. | |
| Apr 15, 2017 at 18:11 | comment | added | Chill2Macht | This comment might not be entirely helpful, but since the multinomial distribution is multivariate, the analog of the variance would be the (co)variance matrix en.wikipedia.org/wiki/Covariance_matrix I am not familiar enough with multivariate statistics to give recommendations for specific tests to use with the covariance matrix. | |
| Apr 15, 2017 at 17:59 | review | First posts | |||
| Apr 15, 2017 at 18:01 | |||||
| Apr 15, 2017 at 17:56 | history | asked | troisquatre | CC BY-SA 3.0 |