Talk:Probit model

message: Anyone willing to give a practical example for quick intuition?  Math only-descriptions are only so helpful sometimes.
160.39.191.94 (talk) 20:42, 19 April 2018 (UTC)
thread_title:  Practical Example 

message: Can I get a bit more background? What is the probit model? What does it do? What was it developed for? How was it developed? As is, the article provides minimal explanatory text before launching into near gibbrish.Theblindsage (talk) 08:19, 26 November 2013 (UTC)
thread_title:  Background 

message: 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)
replies: text: Where is the cite in M&N (i.e. what page?) Pdbailey 02:18, 12 June 2007 (UTC)
replies: text: 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)
replies: text: 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)
replies: text:  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)
thread_title:  Citation for Bliss 

message: In the section of the same name in the article, what the heck is going on? What is the beta value that is being estimated? The article now reads
 Its advantage is the presence of a closed-form formula for the estimator, and possibility to carry out analysis even when individual observations are not available, only their aggregated counts r_t, n_t, and x_{(t)} (for example in the analysis of voting behavior).
But why can't the MLE be found with tabular data in this same situation? What is this advantage over? PDBailey (talk) 02:50, 4 August 2009 (UTC)
replies: text: A possible reference for Berkson’s method is «». The β being estimates is exactly the same beta which was in the definition of the model: P[Y=1|X] = Φ(X ′β). The presence of closed-form solution is indeed an advantage, as it is generally more difficult to implement the maximization routine. As for the “applicability to tabular data” — I’m not sure about that, better to find the original article. ... stpasha » talk » 07:50, 4 August 2009 (UTC)
replies: text: Thanks, so the advantage is a disadvantage then and I just added a fact tag to the claims about the estimator. If you know the book has these claims in it, you could add it. PDBailey (talk) 01:21, 5 August 2009 (UTC)
thread_title:  Berkson's minimum chi-square method 

message: Dr. Winkelmann has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:
We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.
We believe Dr. Winkelmann has expertise on the topic of this article, since he has published relevant scholarly research:
*Reference : Rainer Winkelmann, 2009. "Copula-based bivariate binary response models," SOI - Working Papers 0913, Socioeconomic Institute - University of Zurich.
ExpertIdeasBot (talk) 16:57, 27 July 2016 (UTC)
thread_title:  Dr. Winkelmann's comment on this article 

message: I think it would be very helpful to mention the distinctions between a probit model and logistic regression, which is the most popular classification model in machine learning. The picture I have is:
Logistic regression models log odds as a linear function of the predictors; Probit models odds (?) as a linear function of predictors
Logistic regression creates a linear decision boundary or hyperplane; Probit creates a nonlinear decision boundary (? of what functional form? or is it simply a different linear decision boundary? If probit is a generative function that returns a probability for any set of predictors, what would be the discriminant function that returns a binary indicator?)
When would one use a probit model vs. logistic regression? When one has a very clear picture that odds, not log-odds, are a linear function of the predictors?
98.14.249.94 (talk) 12:00, 20 June 2018 (UTC)
thread_title:  Differences vs. Logistic Regression Model 

message: The article Binary response model with latent variable is short and poorly written, but it contains some content about dealing with heteroskedasticity and misspecification in probit models which might be useful here (for example, a 'Heteroskedasticity' section). I propose that the useful content from the other article be merged here. (Then the article can be redirected to binary choice model or binomial regression). Wikiacc (¶) 22:42, 21 May 2019 (UTC)
replies: text: Instead of merging, I just copied the text on misspecification here. It requires some cleanup. Wikiacc (¶) 18:20, 16 June 2019 (UTC)
thread_title:  Merge from Binary response model with latent variable