Timeline for How to determine if one fit is significantly better than a slightly different fit?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| Oct 11, 2012 at 21:52 | vote | accept | Stuart Robbins | ||
| Oct 11, 2012 at 21:52 | comment | added | Stuart Robbins | Okey dokey, I've talked with a statistics master's student who I'm working with on another project and she's agreed to help with this and is thinking about doing some jack-knifing and boot-strapping and other tests to see what's what. Thanks! | |
| Sep 8, 2012 at 20:25 | comment | added | Michael R. Chernick | @StuartRobbins The likelihood is just a given function of the parameter(s) using the observed data. You find its maximum which gives the maximum likelihood estimates. For the Akaike criterion plug in the maximum likelihood estimate(s) inot the likelihood to get the value of AIC. | |
| Sep 8, 2012 at 20:02 | comment | added | Stuart Robbins | Thanks, AICc (not AIC_C as I said originally) is just a modification that further penalizes you due to additional fit parameters, as you said. My question is more on how to evaluate that likelihood part -- my reference is the "Data Reduction and Error Analysis" book by Bevington & Robinson and I'm trying to follow them, but I'm stuck at exactly what I'm evaluating in the sum (sum 'cause it's ln(L)). Is this a separate question to ask? I didn't happen to see it when doing a search on this site. | |
| Sep 8, 2012 at 4:50 | comment | added | Michael R. Chernick | That is a modification to the original AIC. I am not familiar with it. But this entire class of criteria are all similar and all involve maximizing the likelihood - a penalty. I am think of the form -2 log likelihood because that is how a likelihood ratio test comparing two models is expressed. That is because that test statistic has an asymptotic chi square distribution under the null hypothesis. So I think that is why the criterion (e.g. AIC) is expressed in this form. | |
| Sep 8, 2012 at 4:28 | comment | added | Stuart Robbins | I'm looking at AIC_C at the moment, and running into confusion (as usual) with the likelihood function. I already have my fit parameters, so am I just doing ln(L) where L is the sum of ln(my fit function, either exp-lin or exp-quad) summed for each data point? All the derivative stuff is just for finding the max which I don't need to do here since I have the fit parameters, right? | |
| Sep 7, 2012 at 16:53 | history | answered | Michael R. Chernick | CC BY-SA 3.0 |