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RE: st: gradient and the inverse of the information matrix


From   Jun Xu <mystata@hotmail.com>
To   Listserv STATA <statalist@hsphsun2.harvard.edu>
Subject   RE: st: gradient and the inverse of the information matrix
Date   Fri, 3 May 2013 16:33:43 -0500

Chi-lin,

Again, I want to congratulate you on writing a real nice expository piece that gives many practical details about how to conduct the score test, and I hope it can be published soon in the Stata Journal or other good outlets.

Jun

----------------------------------------
> From: kirin_guess@yahoo.com.tw
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: gradient and the inverse of the information matrix
> Date: Fri, 3 May 2013 13:23:39 +0100
>
> I have a manuscript that is exactly related to your question. The article
> also briefs some possible reasons why the score test conducted by SAS and
> STATA can be different. (See the footnote 6.)
> You can download the article from:
> https://docs.google.com/file/d/0B984NoKuZv46akZPeGRKcTZHekU/edit?usp=sharing
>
> Chi-lin Tsai
>
>
>
> -----Original Message-----
> From: Jon Mu
> Sent: Wednesday, May 01, 2013 10:01 PM
> To: Listserv STATA
> Subject: st: gradient and the inverse of the information matrix
>
> Hi Statalisters,
>
> I am trying to check into the (Rao's) score (or commonly known as the
> Lagrange Multiplier) test for a model that I am working on. I got results
> from SAS already, and I want to see if those from SAS would square with the
> one produced from my own Stata codes.
>
> They don't match, and looks like I probably made some mistakes in my Stata
> codes. For the generalized formula to get the Chi-Square statistic, I need
> to get the gradient and the inverse of the information matrix. For the
> inverse of the information matrix, I can grab from e(V) directly without any
> further calculation.
>
> So I might've made some mistake in the gradient. I've searched through the
> voluminous Stata pdf documentation using gradient as the key word, and I was
> not able to find useful information. But I vaguely remember a while back ago
> when I was also checking into related issues, I read somewhere that the
> e(gradient) matrix is a gradient with respect to xb, not b, so I suspect
> that might be the cause. I am wondering if that's the case. If I am right on
> this, then a follow-up question is how to recover the gradient with respect
> to b since I feel there might not be a linear transformation that I can use
> to get it directly. Any input/suggestion would be appreciated.
>
> Jun Xu, PhD
> Associate Professor
> Department of Sociology
> Ball State University
> Muncie, IN 46037
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