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isn't this just the same as Generalized linear model? Gtx from the country of the football championships ;-) Frank1101 21:13, 28 June 2006 (UTC)
Why is the "Application" section give one example from neuroimaging that is less-than-obvious, when the GLM underlies almost all simple statistical tests and therefore has a plentiful supply of clear and easy-to-understand examples? --user: NotTires —Preceding unsigned comment added by 169.237.26.204 ( talk) 01:04, 12 July 2008 (UTC)
The above says "it is standard" but there is no citation. Kendall&Stuart use "general linear regression model" for what is is otherwise called "multiple regression" ie a univariate independent variable. I believe I have seem "multivariate regression" used generally for the problem as described in the article: for example by Zellner(1971) An introduction to Bayesian Inference in Econometrics, Wiley ISBN 0-471-98165-6. But I cannot find this in my stats dictionaries. However "General linear model" does not appear either. So, any citations for the term "General linear model" used in the sense of the present article? Melcombe ( talk) 16:37, 13 March 2009 (UTC)
OK, I have found a basic early explicit definition in the introductory text book by Mood & Graybill, which only covers the univariate nultiple regression case, and some of the references in the first list above do extend this meaning to cover multivariate regression but without I formal definition. (Some of the other lists are not publically accessible.) But what is the supposed distinction between "general linear model" and "generalized linear model": ie what supposedly makes "generalized linear model" more general? The definition of Mood & Graybill allows for non-normal additive residuals and I suppose something could be constructed to show that "generalized linear model" is more general, but does someone have a citation that does explicitly make an acceptable statement of why "generalized linear models" are more general? As for "general linear model", if too many people are using the terminology in too many different senses, perhaps this needs to be recognised. Of course there is also the question of "general linear model hypothesis" which seems not yet covered on wikipedia and which is again subtly different in meaning. Melcombe ( talk) 09:06, 3 April 2009 (UTC)
isn't this just the same as the linear model? -- Sineuve ( talk) 12:34, 19 March 2010 (UTC)
I made a change that may answer this and discusses how I play/hope to develop this. My understanding is based partly on the initial formula attributed to Mardia. In this case we have multiple independent variables. So we have multiple linear regression generalized to allow for multiple independent variables. This is also the term used in. [1]
Regardless of what it is called, we can consider the number of dependent variables, consider correlated error terms, non-normality, and heterogeneity the number of independent variables, and the use of non-linear link functions.
On this page, I plan to start with linear regression with multiple dependent variables and generalize to multiple independent variables as in [1].
I noticed Seber refers to generalized least squares when dealing with correlated errors—weighted least squares, this seems to match what Mood and Graybill.
I do not see a simple development of multiple linear regression with one independent variable, on wikipedia Please tell me if I'm wrong.
Having said that, You see my initial plan, but I'm not 100% sure what should go on this page.
What do you reckon?
FordPrefect1979 ( talk) 02:03, 19 December 2011 (UTC)
You mean that multiple dependent variables are the difference between the general linear model (aka multivariate linear regression) and multiple linear regression.
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![]() | This article is rated Start-class on Wikipedia's
content assessment scale. It is of interest to the following WikiProjects: | ||||||||||
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isn't this just the same as Generalized linear model? Gtx from the country of the football championships ;-) Frank1101 21:13, 28 June 2006 (UTC)
Why is the "Application" section give one example from neuroimaging that is less-than-obvious, when the GLM underlies almost all simple statistical tests and therefore has a plentiful supply of clear and easy-to-understand examples? --user: NotTires —Preceding unsigned comment added by 169.237.26.204 ( talk) 01:04, 12 July 2008 (UTC)
The above says "it is standard" but there is no citation. Kendall&Stuart use "general linear regression model" for what is is otherwise called "multiple regression" ie a univariate independent variable. I believe I have seem "multivariate regression" used generally for the problem as described in the article: for example by Zellner(1971) An introduction to Bayesian Inference in Econometrics, Wiley ISBN 0-471-98165-6. But I cannot find this in my stats dictionaries. However "General linear model" does not appear either. So, any citations for the term "General linear model" used in the sense of the present article? Melcombe ( talk) 16:37, 13 March 2009 (UTC)
OK, I have found a basic early explicit definition in the introductory text book by Mood & Graybill, which only covers the univariate nultiple regression case, and some of the references in the first list above do extend this meaning to cover multivariate regression but without I formal definition. (Some of the other lists are not publically accessible.) But what is the supposed distinction between "general linear model" and "generalized linear model": ie what supposedly makes "generalized linear model" more general? The definition of Mood & Graybill allows for non-normal additive residuals and I suppose something could be constructed to show that "generalized linear model" is more general, but does someone have a citation that does explicitly make an acceptable statement of why "generalized linear models" are more general? As for "general linear model", if too many people are using the terminology in too many different senses, perhaps this needs to be recognised. Of course there is also the question of "general linear model hypothesis" which seems not yet covered on wikipedia and which is again subtly different in meaning. Melcombe ( talk) 09:06, 3 April 2009 (UTC)
isn't this just the same as the linear model? -- Sineuve ( talk) 12:34, 19 March 2010 (UTC)
I made a change that may answer this and discusses how I play/hope to develop this. My understanding is based partly on the initial formula attributed to Mardia. In this case we have multiple independent variables. So we have multiple linear regression generalized to allow for multiple independent variables. This is also the term used in. [1]
Regardless of what it is called, we can consider the number of dependent variables, consider correlated error terms, non-normality, and heterogeneity the number of independent variables, and the use of non-linear link functions.
On this page, I plan to start with linear regression with multiple dependent variables and generalize to multiple independent variables as in [1].
I noticed Seber refers to generalized least squares when dealing with correlated errors—weighted least squares, this seems to match what Mood and Graybill.
I do not see a simple development of multiple linear regression with one independent variable, on wikipedia Please tell me if I'm wrong.
Having said that, You see my initial plan, but I'm not 100% sure what should go on this page.
What do you reckon?
FordPrefect1979 ( talk) 02:03, 19 December 2011 (UTC)
You mean that multiple dependent variables are the difference between the general linear model (aka multivariate linear regression) and multiple linear regression.
{{
cite journal}}
: Cite journal requires |journal=
(
help)