Timeline for What Are Nuisance Variables (and Parameters)?
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 14, 2017 at 19:04 | vote | accept | EJ16 | ||
| Apr 13, 2017 at 14:17 | comment | added | whuber♦ | @Michael Since you know about GEE, why not illustrate your answer with a simple GEE situation? | |
| Apr 13, 2017 at 2:50 | comment | added | Michael R. Chernick | @MonicaW. So is my answer starting to look good to you? I know about GEE (at least I used to). The within subject correlation is a distribution parameter. An estimate of it is a variable. I guess it is not the within subject correlation that is the objective of the analysis. Hence it can be a nuisance parameter. | |
| Apr 13, 2017 at 2:30 | comment | added | EJ16 | GEE is a quasi-likelihood model. It relaxes the assumptions of the joint distributions so MLE is not required. GEE is especially popular with repeated observations. I'm assuming this is how it also associated with the nuisance, since with MLE you pick good parameters with it. So, it's finally starting to make some sense now given the focus on parameters like you said. But they say that with GEE the w/in subject correlation is treated as a nuisance variable. I guess that's the part I'm not totally getting. | |
| Apr 13, 2017 at 1:58 | comment | added | Michael R. Chernick | I think you should consider it a parameter rather than a variable. When the goal is to estimate the mean, the variance is of no interest but is an obstacle to achieve the goal. So you are correct. I do not know what the case is with generalized estimating equations. | |
| Apr 13, 2017 at 1:53 | comment | added | EJ16 | I read one definition of a nuisance variable of being one that is related to the dependent variable but is of no experimental interest. How does GEE assume the variables to all be nuisance? I'm just not getting it for some reason. Because it's just estimating the means? | |
| Apr 13, 2017 at 1:44 | comment | added | Michael R. Chernick | The t distribution doesn't depend on the nuisance parameter. So in this specific case we can estimate the mean and compute confidence intervals for the mean. I picked this example because the population variance is the nuisance parameter. | |
| Apr 13, 2017 at 1:40 | comment | added | EJ16 | Michael, thank you. But I'm not entire sure why I can't wrap my head around this one. You lost me at the t-distribution. | |
| Apr 13, 2017 at 1:33 | history | answered | Michael R. Chernick | CC BY-SA 3.0 |