solved through a private chat with Dason.
hey everyone! just a quicky-quick question here.
i think i remember reading somewhere that some of the nice properties of parameter estimates obtained via maximum likelihood get lost when you choose the wrong likelihood function over which you should me maximising. say the true data-generation model requires you to choose... i dunno, a beta or a gamma likelihood but you incorrectly choose to maximize over a gaussian likelihood.
does anyone remember what gets lost? what remains? what changes? i'm aaalmost sure that you keep consistency but you lose the efficiency property if you leave it as it is but i cant.freakin.find.the.chapter.where.i.read.this.
ideas or references of where to look for this stuff are appreciated
thanks peeps!
(ps- i'll mention y'all who helped me on the dedication section of my thesis. and will get you a virtual cupcake <-- (maybe)
for all your psychometric needs! https://psychometroscar.wordpress.com/about/
solved through a private chat with Dason.
for all your psychometric needs! https://psychometroscar.wordpress.com/about/
But if we are typing very slowly?
Pawitan (2001) In all likelihood page 370, says under ”Maximum likelihood under a wrong model”:
Then Pawitan shows a number of examples, among them a gamma model estimated with a normal distribution model.“Therefore, maximizing the likelihood is equivalent to finding the best model, the one closest to the true distribution in the sense of the Kullback-Leibler distance.”
Maximum likelihood works well for a correct model under regularity conditions, when likelihood can be approximated by a quadratic function. But regularity conditions are not fulfilled for an example with a uniform distribution when the parameter is a boundary parameter.“Thus the mean and variance of the true distribution is consistently estimated. This is an example where a ‘wrong’ model would still yield consistent estimates of useful population parameters. Such estimates are said to be robust with respect to model mis-specification.
Using a wrong mode, we will generally get biased or inconsistent estimates, but we might also lose efficiency.”
spunky (09-20-2012)
oh well, a quicky-quick question can also warrant a long and tedious answer... but i'm prepared for that.
and greta, thank you very much for everything (now i can even provide an refernce for that part of my thesis).
your command of statistics never ceases to amaze me. i really like reading your posts, i learn quite a bit from them
for all your psychometric needs! https://psychometroscar.wordpress.com/about/
I've heard God kills a kitten if you're wrong. Hope this helps or at least provides a smile (no need to give me credit in your thesis though).
"If you torture the data long enough it will eventually confess."
-Ronald Harry Coase -
uhmm... are you sure? i've only heard of something like that in this context
for all your psychometric needs! https://psychometroscar.wordpress.com/about/
True but there are many kinds. See LINK
Again please don't quote me there's no need. I'm just glad to provide assistance.
"If you torture the data long enough it will eventually confess."
-Ronald Harry Coase -
I'll add that I'm certainly hoping the consequences aren't too severe since I'm not entirely convinced that anybody has ever really chosen a 100% correct likelihood ever for any non-trivial data.
I don't have emotions and sometimes that makes me very sad.
Oh really? I'd love to hear about it.
I don't have emotions and sometimes that makes me very sad.
you'll have to give me a cupcake.
i am in possession of THE only dataset ever known to humankind where the correct likelihood was fit to the data...
for all your psychometric needs! https://psychometroscar.wordpress.com/about/
Spunky: [MATH]\hspace{5in}[/MATH] tricks the box so you can get away ith less characters and not have it show up. I'll prove it in the next post (now we've truly derailed this thread)
"If you torture the data long enough it will eventually confess."
-Ronald Harry Coase -
@spunky
Oh, please skip the dedication!
And give the cakes to Jake, who is, I believe, a “cookie scientist”.
To bring back this discussion a little bit to the original post I would like to ask this:
What is the meaning of this:
And what did you mean by this expression (something like this):Dason on the Cauchy distribution:
"YOU BETTER LOOK OUT BECAUSE THIS IS SOMETHING THAT IS GOING TO GET YOU"
What means:“Frequentism used to be cool but then they got a knife in their knee.”
Does it mean: “Working for the Raptors”?“Workin’ for the Raptors”
I understand (nowdays, but I didn’t before) that “raptors” is not “eagles and hawks”, but rather velociraptors, some kind of ancient dinosaurs. All of this can be very confusing for new readers.
(Why don’t you ever use upper case letters in the beginning of a sentence?)
I was gooling the expression: “Maximum likelihood under a wrong model” and found many links.
Edit:
Thanks for friendly words! I hope this is not considered confrontational. I am just curious and want to know.
Last edited by GretaGarbo; 09-21-2012 at 11:15 AM.
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