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RE: st: Query..


From   "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Query..
Date   Tue, 16 Apr 2013 23:01:50 +0000

The context i was referring to was an old article by George Box in Biometrika aboutg 1953 in which he commented that testing for heteroskedasticy was like setting to see in a rowboat to see if it was safe for the Queen Mary to sail.  Sorry i don't have the quote, and my books are all bundled up due to a flood in my basement

Peter A. Lachenbruch,
Professor (retired)
________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of John Antonakis [John.Antonakis@unil.ch]
Sent: Tuesday, April 16, 2013 1:47 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Query..

Hello Peter:

Can you please elaborate? The chi-square test of fit--or the likelihood
ratio test comparing the saturated to the target model--is pretty
robust, though as I indicated, it does not behave as expected at small
samples, when data are not multivariate normal, when the model is
complex (and the n to parameters estimated ration is low). However, as I
mentioned there are remedies to the problem. More specifically see:

Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit
measures in structural equation models. Sociological Methods & Research,
21(2), 205-229.

Herzog, W., & Boomsma, W. (2009). Small-sample robust estimators of
noncentrality-based and incremental model fit. Structural Equation
Modeling, 16(1), 1–27.

Swain, A. J. (1975). Analysis of parametric structures for variance
matrices (doctoral thesis). University of Adelaide, Adelaide.

Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for
mean and covariance structure analysis with nonnormal missing data. In
M. E. Sobel & M. P. Becker (Eds.), Sociological Methodology (pp.
165-200). Washington, D.C: ASA.

In addition to elaborating, better yet, if you have a moment give us
some syntax for a dataset that you can create where there are
simultaneous equations with observed variables, an omitted cause, and
instruments. Let's see how the Hansen-J test (estimated with reg3, with
2sls and 3sls) and the normal theory chi-square statistic (estimated
with sem) behave (with and with robust corrections).

Best,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________

On 16.04.2013 22:04, Lachenbruch, Peter wrote:
> I would be rather cautious about relying on tests of variances.  These are notoriously non-robust.  Unless new theory has shown this not to be the case, i'd not regard this as a major issue.
>
> Peter A. Lachenbruch,
> Professor (retired)
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of John Antonakis [John.Antonakis@unil.ch]
> Sent: Tuesday, April 16, 2013 10:51 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Query..
>
> In general I find Acock's books helpful and I have bought two of them.
> The latest one he has on SEM was gives a very nice overview of the SEM
> module in Stata. However, it is disappointing on some of the statistical
> theory, in particular with respect to fact that he gave too much
> coverage to "approximate" indexes of overidentification, which are not
> tests, and did not explain enough what the chi-square statistic of
> overidentification is.
>
> The Stata people are usually very good about strictly following
> statistical theory, as do all econometricians, and do not promote too
> much these approximate indexes.  So, I was a bit annoyed to see how much
> airtime was given to rule-of-thumb indexes that have no known
> distributions and are not tests. The only serious test of
> overidentification, analogous to the Hansen-Sargen statistic is the
> chi-square test of fit. So, my suggestion to Alan is that he spends some
> time to cover that in the updated addition and not to suggest that
> models that fail the chi-square test are "approximately good."
>
> For those who do not know what this statistic does, it basically
> compares the observed variance-covariance (S) matrix to the fitted
> variance covariance matrix (sigma) to see if the difference (residuals)
> are simultaneously different from zero. The fitting function that is
> minimized is:
>
> Fml =  ln|Sigma| - ln|S| + trace[S.Sigma^-1] - p
>
> As Sigma approaches S, the log of the determinant of Sigma less the log
> of the determinant of S approach zero; as concerns the two last terms,
> as Sigma approaches S, the inverse of Sigma premultiplied by S makes an
> identity matrix, whose trace will equal the number of observed variables
> p (thus, those two terms also approach zero). The chi-square statistic
> is simply Fml*N, at the relevant DF (which is elements in the
> variance-covariance matrix less parameters estimated).
>
> This chi-square test will not reject a correctly specified model;
> however, it does not behave as expected at small samples, when data are
> not multivariate normal, when the model is complex (and the n to
> parameters estimated ration is low), which is why several corrections
> have been shown to better approximate the true chi-square distribution
> (e.g., Swain correction, Yuan-Bentler correction, Bollen-Stine bootstrap).
>
> In all, I am thankful to Alan for his nice "how-to" guides which are
> very helpful to students who do not know Stata need a "gentle
> introduction"--so I recommend them to my students, that is for sure.
> But, I would appreciate a bit more beef from him for the SEM book in
> updated versions.
>
> Best,
> J.
>
> __________________________________________
>
> John Antonakis
> Professor of Organizational Behavior
> Director, Ph.D. Program in Management
>
> Faculty of Business and Economics
> University of Lausanne
> Internef #618
> CH-1015 Lausanne-Dorigny
> Switzerland
> Tel ++41 (0)21 692-3438
> Fax ++41 (0)21 692-3305
> http://www.hec.unil.ch/people/jantonakis
>
> Associate Editor
> The Leadership Quarterly
> __________________________________________
>
> On 16.04.2013 17:45, Lachenbruch, Peter wrote:
>   > David -
>   > It would be good for you to specify what you find problematic with
> Acock's book.  I've used it and not had any problems - but maybe i'm
> just ancient and not seeing issues
>   >
>   > Peter A. Lachenbruch,
>   > Professor (retired)
>   > ________________________________________
>   > From: owner-statalist@hsphsun2.harvard.edu
> [owner-statalist@hsphsun2.harvard.edu] on behalf of Hutagalung, Robert
> [Robert.Hutagalung@med.uni-jena.de]
>   > Sent: Monday, April 15, 2013 2:06 AM
>   > To: statalist@hsphsun2.harvard.edu
>   > Subject: AW: st: Query..
>   >
>   > Hi David,
>   > Thanks, though I find the book very useful.
>   > Best, Rob
>   >
>   > -----Ursprüngliche Nachricht-----
>   > Von: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von David Hoaglin
>   > Gesendet: Samstag, 13. April 2013 16:11
>   > An: statalist@hsphsun2.harvard.edu
>   > Betreff: Re: st: Query..
>   >
>   > Hi, Rob.
>   >
>   > I am not able to suggest a book on pharmacokinetics/pharmacodynamics,
>   > but I do have a comment on A Gentle Introduction to Stata.  As a
> statistician, I found it helpful in learning to use Stata, but a number
> of its explanations of statistics are very worrisome.
>   >
>   > David Hoaglin
>   >
>   > On Fri, Apr 12, 2013 at 9:01 AM, Hutagalung, Robert
> <Robert.Hutagalung@med.uni-jena.de> wrote:
>   >> Hi everyone, I am a new fellow here..
>   >> I am wondering if somebody could  a book (or books) on Stata dealing
> with pharmacokinetics/pharmacodinamics - both analyses and graphs.
>   >> I already have: A Visual Guide to Stata Graphics, 2' Edition, A
> Gentle Introduction to Stata, Third Edition, An Introduction to Stata
> for Health Researchers, Third Edition.
>   >> Thanks in advance, Rob.
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