Talk:Probit model

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Citation for Bliss

The citations for Bliss's papers are already in the article. I added McCullagh & Nelder which claims this was developed by Bliss. I thus then removed the cite needed tag. Baccyak4H (Yak!) 17:39, 11 June 2007 (UTC)[reply]

Where is the cite in M&N (i.e. what page?) Pdbailey 02:18, 12 June 2007 (UTC)[reply]

From the second edition (1989), top of pg 13 (beginning of section 1.2.5): "The technique known as probit analysis arose in conection with bioassay, and the modern method of analysis dates from Bliss (1935)." Baccyak4H (Yak!) 13:05, 12 June 2007 (UTC)[reply]

The same book (p 43) says that the method of solving the probit was in an appendix to the same article, written by Fisher. How can one be so sure that Fisher didn't influence Bliss? Pdbailey 13:10, 12 June 2007 (UTC)[reply]

I read that ref to imply Fisher's contribution was using the scoring method in the context of the model itself. However, without immediate access to the paper, that is just my best educated interpretation/guess. If reading the paper in its entirety suggests Fisher could get co-credit (or even most or all of the credit) for the model itself, this article should reflect that. If you an get a copy, that would be great. 17:50, 12 June 2007 (UTC)

X prime?

Is x prime correct? I've always seen it xB, not x'B for the classical linear model.Aaronchall (talk) 04:54, 8 January 2008 (UTC)[reply]

wording issues

This is a pretty dense little article. I think a gentler intro should be developed. But more specifically now, I take issue with some wordings that are ambiguous or potentially a bit haughty:

"Because the response is a series of binomial results, the likelihood is often assumed to follow the binomial distribution.": What does that mean? I myself understand how there is an assumption of normally distributed unobservable errors in the latent variable formulation of this model, but how or where is there a binomial distribution assumption in the model?

"The parameters β are typically estimated by maximum likelihood." Actually, how else can they be understood or estimated? I have the impression that there are different ways that this or any other maxiumum likelihood model parameters can be found, numerically. But it is a maximum likelihood model, so what is meant by the suggestion this is "typically" but not always a maximum likelihood model. I am clearly missing something, or the language is imprecise.

"While easily motivated without it, the probit model can be generated by a simple latent variable model." Easily motivated by whom / how? I object to the "easily" word.

"Then it is easy to show that" should be changed to Then it can be shown that". Easy is subjective, and I think it comes across wrong to general readers of the encyclopdia, the vast majority of whom will not find anything easy about showing that. doncram (talk) 23:40, 21 May 2009 (UTC)[reply]

What do you mean by saying that it's a "maximum likelihood model"? There's nothing in the model itself that says anything about maximum likelihood, and one can readily imagine methods other than maximum likelihood for estimating the parameters. For example, if one has a prior probability distribution of the parameters, then one could use the posterior expected values of the parameters as estimates. You are right to say that you're clearly missing something. I don't think the language at that point is imprecise. Michael Hardy (talk) 00:43, 22 May 2009 (UTC)[reply]

Hmm, thanks for responding, that helps me a bit. As a reader, I am really already invested in understanding it as a maximum likelihood model. Given data, I can't really absorb how you could (and why you would) choose any other method of estimating the model, besides trying to figure out what are the parameters that are most likely to have resulted in the observed data (given an assumption of normal errors in the latent variable model). You suggest that i could also want to take into account a prior distribution. But then, I absorb that only as a broader maximum likelihood problem: there was previous data that is summarized in some informed priors, and then there is some new data. I don't exactly know how to do this necessarily, but I would want to use a maximum likelihood approach to combine the priors and new data to come up with new estimates. I wonder then: Is there a non-MLE based approach (which would also have a Bayesian perspective extension)? Is there some non-MLE approach to estimating the parameters of the model that has ever been used for practical purposes? In a regular linear regression context, i do understand other alternatives, but here i do not. doncram (talk) 01:23, 22 May 2009 (UTC)[reply]

P.S. I see you edited the article to remove the sentence about the binomial distribution, and to remove the two "it is easy" assertions. Thanks! However, the math display is all messed up now. doncram (talk) 01:27, 22 May 2009 (UTC)[reply]

Doncram, in addition to Frequentist statistics (which has things like the MLE) there is Bayesian statistics (which has things like priors). PDBailey (talk) 01:55, 22 May 2009 (UTC)[reply]