Timeline for Bivariate/multivariate models for multinomial response variables
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Jun 4, 2018 at 18:13 | comment | added | David Dale | If these common conditions are reflected in $X$, then slope coefficients for "rainy" and "humid" classes will be similar. If these common conditions are not fully reflected in $X$ (that is, unobservable), then no model will be able to discriminate between this classes, and both $P(rainy|X)$ and $P(humid|X)$ will be high. Any model which is flexible enough can account for such a dependence. | |
| Jun 4, 2018 at 16:57 | comment | added | Fred | What I mean is what if there is an actual correlation among classes of the response variable? For example, assume a response variable "weather" with the classes of "clear, rainy, humid". Clearly the two latter classes are more probable to be dependent on more similar conditions, and consequently may be correlated with a higher degree than with the first class. This seems to be the case of my 9 classes (since for example a1b1 and a1b2 are probably more correlated than a1b1 and a2b2). How do I address this? | |
| Jun 4, 2018 at 6:44 | comment | added | David Dale | When you speak about correlation between categorical variables, I assume that you mean that your outputs $y_1$ and $y_2$ are dependent conditionally on the inputs $x$. By modeling their conditional distribution $P(y_1, y_2 | x)$ as a complete $3\times3$ matrix, you can capture any dependence between them. | |
| Jun 3, 2018 at 20:14 | comment | added | Fred | How does this structure allow for any kind of correlation between the response variables? Isn't IIA (Independence of Irrelevant Alternatives) the main flaw of multinomial logit models (and it seems to be likely to exist in my case, as the classes are all manually created and are clearly correlated)? | |
| May 30, 2018 at 7:53 | history | answered | David Dale | CC BY-SA 4.0 |