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Date:         Sat, 3 Oct 1992 12:59:45 EDT
From:         "Bruno D. Zumbo" <ZUMBO@ACADVM1.UOTTAWA.CA>
Subject:      leverage

Dear Stat-l'ers,
My doctoral class in multivariate statistics has a question which I
(and them since they are writing this with me) cannot answer.  We
have been dealing with leverage values (the diagonals of the hat-
matrix) in multiple regression.

Our question is the following:
  Most statistical packages calculate the hat-matrix by
  X*INV(X'X)*X' well we then applied recommendations by
  Huber (1981) or Belsley et al. on interpreting the
  leverage values.  Well, low and behold SPSS calculates
  what they seem to call an 'adjusted or centered leverage'.
  The result of course is a set of leverage values which
  are smaller than they should be  -- hence, Huber etc.
  -- are not directly applicable.  What the heck is SPSS
  doing and why?  Do any other programs do this?
  We know minitab calculates them correctly, but SPSS
  we cannot figure out.

We would appreaciate any feedback!

Prof. Bruno Zumbo and the Multivariate Class of 1992.
Bruno

Bruno D. Zumbo            * I see the eigenvalue in thine eye,
Measurement & Evaluation  * I hear the tender tensor in thy sigh.
Faculty of Education      * Bernoulli would have been content to die
145 Jean-Jacques Lussier  * Had he but known such a-squared cos 2(phi)!
University of Ottawa      *              -- Stanislaw Lem, "Cyberiad"
Ottawa, Ontario           *
Canada K1N 6N5            *
Phone (613) 564-4222      *
FAX   (613) 564-9098      *
e-mail address: Zumbo@acadvm1.Uottawa.ca
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Date:         Wed, 30 Sep 1992 11:01:38 EDT
From:         HAINES@NEWSCHOOL.EDU
Subject:      VAR and Panel Software

Soon I will be starting two projects each requiring quite different
types of analysis: One time series and the other pooled cross-section
time-series (panel) data set. I am interested in peoples opinion
regarding the latest (best) software available to analyze these data
sets.  The time series software requires VAR (and preferably VARMA)
capabilities with flexibility on decomposition techniques (SVARs),
IRFs, VDCs, and full diagnostics.  I am flexible on the operating
platform, but prefer PC compatible.

I am familiar with RATS386 v3.1, SPSS, and Micro TSP. So there is
little need to comment on these.  Thanks in advance.

------------------------------------------------------------------
Troy L. Haines                      Internet: HAINES@NEWSCHOOL.EDU
New School For Social Research      Compuserve: 73617,2135
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Date:         Tue, 6 Oct 1992 09:48:23 U
From:         Eileen Driscoll <eileen_driscoll@QMRELAY.MAIL.CORNELL.EDU>
Subject:      point biserial correlation

 point biserial correlation
Hi, Folks.  Does anyone know an easy way to calculate a point biserial
correlation in either SAS or SPSS? I could do it in a data step using PROC
SUMMARY but I am trying to save some time.  Thanks.  Eileen Driscoll
efd@cornellc
From: statlab1@gw.wmich.edu
Newsgroups: bit.listserv.stat-l
Subject: test!
Date: 5 Oct 92 14:35:17 EST
Organization: Western Michigan University

test

only a test!
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Date:         Wed, 30 Sep 1992 14:27:51 EDT
From:         Frank Dane <FDANE@UGA.BITNET>
Subject:      Re: Matching cases
              30 Sep 1992 09:17:00 EST from <BTD0@NIORDS1.EM.CDC.GOV>

Brian Day asked about selecting matching cases from a large data set
to compare with a n=17 data set.  Previous comparisons between n=17
and n=3052 produced "no differences".

Assuming the 17 v. 3052 comparison was theoretically (as opposed to
practically) reasonable, I would argue that matching should not be
employed.  Instead, about 17 cases should be _randomly_ selected from
the large data set.  Equalizing the sample size takes care ofthe
problems inherent with different sized samples, and the random selection
avoids the problem of compounding (and potentially confounding)
interpretation based on the variables selected for matching.

On the other hand,if there are theoretical reasons for matching,
then the initial plan makes sense.  Any results would, of course,
have to be qualified by the theoretical bassis for matching.
Frank Dane

Dr. Francis C. Dane, Associate Professor and Chair
Department of Psychology, Mercer University
Macon, GA  31207-0001  USA
Bitnet: FDANE@UGA   Internet: FDANE@UGA.CC.UGA.EDU
Tel: (912) 752-2972
Fax: (912) 752-2108
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Date:         Sun, 4 Oct 1992 19:53:22 +1000
From:         alan@DMSMELB.MEL.DMS.CSIRO.AU
Subject:      Re: leverage
              03 Oct 92 12:59:45 EDT."
              <9210031707.AA12725@dmsmelb.mel.dms.CSIRO.AU>

I hope it is not true that
  Most statistical packages calculate the hat-matrix by
  X*INV(X'X)*X' well we then applied recommendations by

If you write X'X = R'R where R is an upper-triangular matrix (Cholesky
factorization) then

   X*INV(X'X)*X' = X.INV(R).INV(R').X' = X.INV(R) times its transpose.
Now X.INV(R) can be calculated one row at a time (overwriting the previous
row if you want to save storage) by back-substitution, so that no inversion
is required.   Also, of course, the Cholesky factorization can be formed
without forming the sum of squares and products matrix X'X, using say
Householder reduction, modified Gram-Schmidt or planar rotations, so that
you get a much more accurate result if there is any ill-conditioning.
I believe this method is used in several statistical packages such as
Minitab and Statistix.   Code for this method is contained in Applied
Statistics algorithm AS 274.

Sorry I can't answer your question about the calculations in SPSS.

Alan Miller, Quality Improvement Project
CSIRO Division of Mathematics & Statistics, Melbourne, Australia
Phone: +61 3 542-2266   Fax: +61 3 542-2474  E-mail: alan@mel.dms.csiro.au
Mail: CSIRO DMS, Private Bag 10, Clayton, Vic. 3168, Australia
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Date:         Wed, 30 Sep 1992 04:24:02 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Need respected reference on prediction
              92 3:47 am

> I need a reference concerning the prediction of future events from past data.

Try Macbeth by W. Shakespeare
Newsgroups: bit.listserv.stat-l
From: baer@uwovax.uwo.ca
Subject: Re: factor analysis
Organization: University of Western Ont, London
Date: Wed, 30 Sep 1992 03:02:42 GMT

In article <1992Sep24.165726.1@cc.helsinki.fi>, tarkkonen@cc.helsinki.fi writes:
> In article <STAT-L%92091918350554@VM1.MCGILL.CA>, SERTEL@DGOGWDG1.BITNET (suitbert ertel) writes:
>> Factor analysis: "Varimin" rotation.
>> 
>> I am pondering over a multivariate problem whose solution seems
>> to require a way of factor rotation which until now has
>> apparently never been considered: Factors should be rotated
>> in such a way that they come up with "complex structure" instead
>> of "simple structure". What I need to apply is the opposite of Kaiser's
>> Varimax criterion, i.e. what I suggest to call the Varimin criterion.
If factor invariance is the issue, why not try either target rotations or
confirmatory factor model between-group constraints?
---------------------------------------------------------------------
Douglas Baer, Department of Sociology,
University of Western Ontario, London, Canada N6A 5C2
Internet: BAER@UWOVAX.UWO.CA   

Newsgroups: bit.listserv.stat-l
From: nichols@spss.com (David Nichols)
Subject: Re: leverage
Organization: SPSS Inc.
Date: Tue, 6 Oct 1992 16:07:38 GMT

The centered leverages calculated by SPSS are smaller than those
defined in Belsley, Kuh and Welsch by a factor of 1/N (for unweighted
data), so in order to use their interpretations, add 1/N to the values
from SPSS.

To the follow-up on computations, it is true that it is unlikely that
anyone uses the formula given directly. SPSS uses a symmetric SWEEP
algorithm due to Dempster, operating on the correlation matrix (which
is why the leverages it produces are centered), then rescales at the
end.

--
 David Nichols           Statistical Support Specialist            SPSS, Inc.
 Phone: (312) 329-3684     Internet: nichols@spss.com     Fax: (312) 329-3657
*******************************************************************************
 Any correlation between my views and those of SPSS is strictly due to chance.
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Date:         Mon, 5 Oct 1992 05:36:05 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Archaeologist needs help

> Since I am clearly not making any inferences about an underlying
> population, I prefer to take the position that my collection is a
> population and I am simply asking "Is Cluster 1 significantly
> different in content from Cluster 2", and so forth.

I think herein lies your fallacy. What does "significantly different" mean
in any context other than sampling?  Cluster 1 contains 3 fishhooks and
4 combs; cluster 2 contains 4 fishhooks and 3 combs: they are different.
QED.  You can only talk about "significantly different" if you add some
hypothesis such as, "Both houses were occupied by families with equal
status so should have contained similar quantities of artifacts. Do the
relative numbers obtained accord with random sampling from such a starting
point?"

It is nonsense to talk about the artifacts retrieved as a population,
until you take a sample from it and wish to extrapolate to the whole set
of artifacts.  For example, you might sample from a set of manuscripts to
decide what conservation measures to apply to that whole set.

The artifacts present in a site are always a sample of materials that have
been added to that site over history.  Unfortunately, they are generally a
very small and partial (in the sense of biased and far from random)
selection.  The artifacts recovered are further affected by the methods
used.  Deming uses the word "chunk" for any selection that cannot be
assumed to be a true random representative sample.  The inference from the
chunk to a larger population must be highly qualified by the faith you
have in how the chunk does represent the original material.  For example,
you probably have strong faith that more robust food remains such as
mussel shells will be over-represented in a midden and delicate items
under-represented or absent.  Don't dispair, Deming points out that for
very small samples a chunk may perform better that a random sample, as it
incorporates other expert knowledge.

You can, of course, make quantitative statements about your whole sample
of retrieved artifacts.  For example, 1000 pottery shards had an average
size of xxx with standard deviation yyy.  In doing this, you would correctly
use what the textbooks call "population" measures.  The results are
purely descriptive statements of fact.

--
(R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
Head of Applications    Direct voice:   +44 482 465296
Computer Centre         Voice messages: +44 482 465685
Hull University         Fax:            +44 482 466441
Hull HU6 7RX, U.K.
From: HOFER@USCMVSA.BITNET
Newsgroups: bit.listserv.stat-l
Subject: SWEEP Operator
Date: 6 Oct 92 14:15:00 GMT
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I make use of the SWEEP operator (see Goodnight, 1979) within
a program that calculates ML covariances for data with
missing values. However, I haven't been able to get the
SWEEP to work in the most general case--that is, to run
through the entire series of variables to calculate the
covariances and residual variances and covariances for
each variable.
  Does anyone know of any published Fortran or BASIC programs
that make use of the general SWEEP operator? Has anyone a
general implementation of SWEEP that they would like to
share? Many thanks for any suggestions.
 Scott Hofer
 Psychology, Univ. of Southern California
 <HOFER@MVSA.USC.EDU>
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Date:         Mon, 5 Oct 1992 15:58:14 -0700
From:         JAMES@UCRAC1.UCR.EDU
Subject:      RE: NeXT statistical software

sas sas sas
;
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Date:         Mon, 28 Sep 1992 13:09:00 EDT
From:         QSUN@VCUVAX.BITNET
Subject:      References need about Heckman-Lee Model

I am working on a project needs references for "Heckman-Lee Model" in
health care study. It seems a kind of Bayesian approach. Any comments?

Thank you in advance.


James Q Sun
Medical College of Virginia
E-mail : Qsun@ruby.vcu.edu
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Date:         Mon, 5 Oct 1992 07:58:00 EST
From:         jhh0@PHPDLS1.EM.CDC.GOV
Subject:      Aggregated samples and independence

Antoine Morin writes:

>Say I want to test the hypothesis that the sex ratio ( proportions of
>males and females) is independent of sampling site. I would need to
>analyse a contingency table. Now, is it fair game to do so if animals
>are not sampled individually? For example,if my sampling method involve
>collecting animals in quadrats that are randomly positionned in
>the various sampling sites. Does it matter? What additional assumptions
>am I making if I proceed? What other way of proceeding would you recommend?

As long as each individual, one way or another, has an equal chance of being
selected, then the method you propose will be satisfactory.  One of the
better treatments of cluster sampling (what you are proposing) is in:

     Selvin, S., 1991.  Statistical Analysis of Epidemiologic Data.
        New York, Oxford University Press.

Note that you will have to adjust your sample size to account for the change
in variance due to possible clustering, and that you may also have to adjust
your analysis to give proper weights to the samples, once again to adjust for
the different probabilities of being selected based on cluster.

--------------------------------------------------------------------------
Jim Handsfield                          |       Voice:  (404)639-1703
Centers for Disease Control             |       FAX:    (404)639-1777
Mail Stop G23                           |
1600 Clifton Road, NE                   |
Atlanta, GA 30333                       |     jhh0@phpdls1.em.cdc.gov
Newsgroups: bit.listserv.stat-l
From: kattan@Jane.UH.EDU (Mike Kattan)
Subject: Need respected reference on prediction
Reply-To: kattan@Jane.UH.EDU
Organization: University of Houston
Date: Wed, 30 Sep 1992 03:47:32 GMT

I need a reference concerning the prediction of future events from past data. 
Basically, I would like a book which follows the predictive process from start
to finish, with normative guidelines for "good" (e.g., accurate or beneficial)
prediction.  I would like the reference to discuss the problems which may occur
and affect prediction effectiveness.  My guess is that it would include
problems with sample data (e.g., error, bias, size, missing values), problems
with future data, model building capabilities, cross validation, etc.

While there are lots of introductory statistics books which have limited
discussion of the above, I have a few more specifics to add:

	1. I'm only interested in prediction with multiple independent
variables and 1 dependent variable (i.e., no time series or econometric
modelling).

	2. I'm only interested in prediction, not inference.  

	3. I'd prefer the discussion to be technique independent.  I have
several books with assumptions of linear regression, but I want to know more
about prediction in general.  What kinds of things, like a biased sample I
suppose, would reduce the prediction effectivness of *any* technique?

	4. I also need some cite as to the general acceptance of the reference
by the statistics community.  Some of the references I've looked at differ a
little in coverage.  Why should I choose the recommended reference over all the
others?  I would like to be able to defend the selection of a particular
framework.

Thanks in advance.  I'll post a summary if I get much e-mail response.

___________________________________________________________________________
Mike Kattan                       | Internet: kattan@jetson.uh.edu         
Decision and Information Sciences | Bitnet:   kattan@uhupvm1  kattan@jetson
University of Houston             | Phone:    (713) 743-4734  Fax: 743-4693
===========================================================================
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Date:         Mon, 5 Oct 1992 12:10:57 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      KL expansion ?

I have been asked to find a reference to KL expansion, being a
technique combining some form of principal components with some
clustering method, due to Kahoon (?). The KL is believed to
stand for "Kahoon low (dimensional expansion)".  I've drawn a
complete blank in the books on my shelf and in the library
catalogue and the Citations index, so if I get a blank response
from the list I'll let you know.  Some techniques survive merely
by having a catchy name.

--
(R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
Head of Applications    Direct voice:   +44 482 465296
Computer Centre         Voice messages: +44 482 465685
Hull University         Fax:            +44 482 466441
Hull HU6 7RX, U.K.
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Date:         Tue, 6 Oct 1992 04:24:14 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: KL - answers and thanks.

> clustering method, due to Kahoon (?). The KL is believed to
> stand for "Kahoon low (dimensional expansion)".  I've drawn a
> complete blank in the books on my shelf and in the library

Thanks to:
Wilf Rosenbaum, Robert Burr, Mark D. H. Miller

for the following information and for demonstrating again how
"The Global University" (Email) beats the pants off Chinese whispers.


From: Robert Burr MSEE PhD <bobburr@edu.washington.u>

KL expansion usually refers to Karhunen-Loeve expansion, an optimum linear
expansion of a set of waveforms or images with respect to a set of
data-dependent non-sinusiodal basis functions.  They are particularly
effective for compact expression of cyclo-stationary signals, and so are
often used for data compression of normal ECG and other signals where
there may be strong information for synchronizing the signal in some way.
The ensemble matrix of second moments is generally non-toeplitz (not
diagonally banded), and its eigenstructure may be very concentrated in the
first few dimensions.  However, if there is no synchronizing information
the matrix becomes toeplitz (diagonally banded), and the eigenanalysis
results in basis functions that are a variant of discrete prolate
spheroidal wave functions, which more and more closely resemble traditional
sine wave basis functions as the length of each series in the
ensemble increases.

K-L transforms are a standard against which other linear (and
hopefully less expensive) transform techniques are compared.  A recent
computerized lit search in the engineering domain netted over 2000
references.  I'll just mention one very nice old but balanced treatment:

Orthogonal Transforms for Digital Signal Processing, N. Ahmed and K.R.
Rao, Springer-Verlag 1975, Chapter 9.

From: "Mark D. H. Miller" <METRMDHM@edu.SYR.SUVM>

> ----------------------------------------------------
    Never fear, you WILL find it ... but
    your luck will increase if you have the correct name.

    The name is   Karhunen-Loeve expansion, named after two
    mathematicians (Karhunen  and  Loeve). The subject is
    an orthogonal expansion of a stochastic process.

    I don't have any reference materials at hand, but you might
    try  Loeve 's  book  in the  Springer  yellow-book series.

    I've also seen it covered in several works on statistical
    pattern recognition.

----------------------------------------------------------------

I'm sure my questioner will be most impressed, if not mind-boggled.

Allan.
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Date:         Mon, 5 Oct 1992 06:01:48 EDT
From:         Antoine Morin <JJASB@ACADVM1.UOTTAWA.CA>
Subject:      Aggregated samples and independence

Say I want to test the hypothesis that the sex ratio ( proportions of
males and females) is independent of sampling site. I would need to
analyse a contingency table. Now, is it fair game to do so if animals
are not sampled individually? For example,if my sampling method involve
collecting animals in quadrats that are randomly positionned in
the various sampling sites. Does it matter? What additional assumptions
am I making if I proceed? What other way of proceeding would you recommend?

This may seem a trivial question to you, but I have not been able to
locate a satisfying treatment of the topic.

Thanks in advance

Antoine Morin
University of Ottawa
jjasb.acadvm1.UOttawa.ca                  Honni soit qui mal y pense!
From: mjk1@CEHIEC1.EM.CDC.GOV
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Subject: SUDAAN Stats Package
Date: 5 Oct 92 17:29:00 GMT
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To obtain the software, I believe you need to contact Dr. Babu Shah at RTI in
North Carolina.
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Date:         Thu, 24 Sep 1992 21:57:21 MSZ
From:         suitbert ertel <SERTEL@DGOGWDG1.BITNET>
Subject:      VARIMIN

> If you know what you are looking for, why don,t you perform it yourself.
> I suggest you use 'graphic rotation', do it by visual appearance of the
> twodimensional subspaces.  We do it here by a locat statistical package,
> called Survo.

> - Lauri Tarkkonen

Your suggestion shows that I should give provide more of the
background that made me post in this list my question/request.
I did this by individual postings and at length for Mike Rickman
and Ed  Haupt who responded earlier.  I did not want to fill
space of your computer screen if you are not interested in factor
analysis.

The aim I am pursuing is in fact much more demanding than you are
probably assuming: I am questioning the validity of Thurstone's
criteria for "simple" structure. My claim is that the criteria
he proposed (others like Kaiser just followed on this line using
more sophisticated techniques) did the contrary of what they should
have done. There is no special data or factorial
research project I am working at presently, I just want to know
whether a revised principle of "simple structure" of the sort
I have in mind can be shown to be empirically valid in ordinary
fields of factorial research. My hypothesis is that parsimony on
the level of factorial constructs will increase as a result of
applying VARIMIN for factor rotation.  That's the reason why an
>analytic< solution of the rotational problems involved is
needed.  I am ready to post the reasoning underlying this
approach to anyone individually who will drop down a request.

Suitbert Ertel


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