Timeline for How to transform an exponential density mixture model from independent to dependent terms?
Current License: CC BY-SA 3.0
7 events
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| Mar 26, 2018 at 6:53 | comment | added | Carl | For a hidden Markov model, a mixture distribution found by regression/optimization would not provide any identification as to which $x_i$ arose from which random process. For my question, it is equally ambiguous as to which path a drug molecule took transiting the body. That would not affect, for example, mean residence time, which is path "oblivious." Do you claim in your answer that this model is not a probability model; real question, please answer yes/no? | |
| Oct 18, 2017 at 5:16 | comment | added | Carl | I know that using mixture models is a dead end for pharmacokinetics, my problem is explaining why. Almost all of the pharmacokinetic literature uses univariate multi-exponential mixture models, usually with two or three terms. The best workaround I have found is to use convolution and dump the mixture approach entirely. Question is, can the the mixture approach be extended to include covariance, and most assuredly it can, for example, E1*E2, where E1 is a monoexponential, E2 is biexponential, and * is convolution. Question is, can this be done without convolution? | |
| Oct 18, 2017 at 3:44 | comment | added | Carl | +1 Because you at least gave me a hint as to what to explore further. | |
| Oct 18, 2017 at 3:35 | comment | added | Carl | @Cliff_AB I do not have as a goal using mixture models for anything. I would not go that route. I am exploring if there is enough wiggle room to improve upon them as applied to pharmacokinetics, and if my characterization of them is statistically correct. | |
| Oct 18, 2017 at 1:11 | comment | added | Carl | @Cliff_AB Can you give me an answer for the biexponential mixture case, please? I am finding the cupola link a bit too general for me to make use of. | |
| Oct 18, 2017 at 0:27 | comment | added | Carl | Well, see above for details. There is no contemporaneity. However, an event occurrence is physically associated with one term at a time. | |
| Oct 17, 2017 at 23:24 | history | answered | Cliff AB | CC BY-SA 3.0 |