HDTenEightyP/NetNews huggingface dataset:
Via:          UK.AC.LE; 30 SEP 92 14:27:10 BST
Site:         University of Leicester, Leicester, U.K.
X-Name:       David D Stretch
Phone:        +44 533 522522 ext 3251, or +44 533 523251 (direct line)
Fax:          +44 533 523293
Organisation: Organisation??? Who?? Me??? Don't make me laugh!
X-Address:    Lecturer in Psychology, Dept. of Psychiatry,
X-Address:    Clinical Sciences Building, Leicester Royal Infirmary,
X-Address:    PO Box 65, Leicester, LE2 7LX, United Kingdom.
X-Mailer:     ELM [version 2.3 PL0 (LUT)]
Newsgroups: bit.listserv.stat-l
Date:         Wed, 30 Sep 1992 14:24:59 BST
Reply-To:     dds@leicester.ac.uk
From:         David Stretch <dds@LE.AC.UK>
Subject:      Re: Need respected reference on prediction
              Sep 30, 92 3:47 am

A book I have found particularly useful for prediction purposes, though
it is technical and not as practical as I think you want, is the
following:

Aitchison, J., and Dunsmore,I.R. (1975) Statistical Prediction Analysis.
   Cambridge University Press. ISBN: 0 521 20692 8
   Library of Congress Cat: 74-25649.

I imagine that the non-Bayesians on the list would prefer, if they were in your
shoes, a different approach, though.

David Stretch


  +-----------------------+---------------------------------------------+
  |  Dr. David D Stretch  | First get your facts;   then you can        |
  |  Dept. of Psychiatry  |     distort them at your leisure.           |
  |  dds@leicester.ac.uk  |                                  Mark Twain |
  +-----------------------+---------------------------------------------+
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Date:         Thu, 1 Oct 1992 08:39:55 CDT
From:         "(J. Philip Miller)" <phil@WUBIOS.WUSTL.EDU>
Subject:      Re: Matching cases (fwd)

> J. Philip Miller asked about the "problems" inherent in unequal
> sample sizes.  At the very least, unequal ns increase the likelihood
> of encountering heterogeneity of variance. (Of course, unequal ns

I do not understand this contention.  I agree that it decreases the robustness
of standard tests, but if you can assume that the two groups have equal
variances if you sample to get equal sample sizes, then the parent population
must also have the same variance.  It is not clear how you will get any more
valid result by taking a sample of the larger group.  You will certainly loose
power and because of that loss of power you may be less likely to make a type
I error, but that does not, in general, increase the validity of the test.


> don't guarantee that condition.) When the disparity of the sample
> sizes is >1 order of magnitude (e.g. 17 v. 3200), one is also likely
> to run into the logical (not mathematical) problem of dealing with
> an idiosyncratic sample (the smaller one) with respect to generalizing
> the results of any analyses.  I'm sure others on the list will note
> other problems.
>
I am not sure I understand the term "idiosyncratic sample".  If you mean that
the smaller group is different from the larger one, then that is precisely
what we are testing with our statistical analysis.  If you wonder whether that
difference might be due to a random process, then that is again what the
statistical test is designed to provide.

--
     J. Philip Miller, Professor, Division of Biostatistics, Box 8067
	 Washington University Medical School, St. Louis MO 63110
     phil@wubios.WUstl.edu - Internet  (314) 362-3617 [362-2694(FAX)]
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Date:         Tue, 6 Oct 1992 12:53:54 EDT
From:         Frank Dane <FDANE@UGA.BITNET>
Subject:      Re: point biserial correlation
              6 Oct 1992 09:48:23 U from
              <eileen_driscoll@QMRELAY.MAIL.CORNELL.EDU>

A point biserial correlation can be calculated using the "regular"
or Pearson correlation routine as long as the dichotomous variable
is coded numberically (-1/+1, 0/1, etc.).  In SAS, use PROC CORR.
It's been a while, but I believe there is PEARSON or something
similarin SPSS.

For the record, the point biserial formula printed in most stat books
is nothing more than a derivation of the Pearson formula, simplified
to take advantage of the fact that one variable hasonly two response
options.  There is no such thing as a "point biserial" correlation
vis-a-vis something different from a Pearson correlation.

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:         Wed, 30 Sep 1992 09:17:00 EST
From:         BTD0@NIORDS1.EM.CDC.GOV
Subject:      Matching cases

Stat-L group --

     I have a group of 17 cases that exhibit symptoms of a disease.  They are
from a larger sample of 3052.  Doing mean and correlation comparisons between
the two samples show no differences, I feel, because the n is so small in one
sample.  My question is, Is it ok to match the 17 cases with 3 or 4 cases
from the larger sample for comparison purposes? Then the n of larger sub
sample would be 51 or 68 instead of 3052.  I am VERY curious to hear
responses.  Thanks in advance for prompt attention to this question.

Brian Day
btd@niords1.em.cdc.gov
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Date:         Fri, 2 Oct 1992 11:04:11 U
From:         dick darlington <dick_darlington@QMRELAY.MAIL.CORNELL.EDU>
Subject:      Var(A*B)

                                                                     2:46 PM
  OFFICE MEMO                                                 Time:
                      Subject:
                      Var(A*B)                                      09-26-92
                                                              Date:

I'm not sure of all Kun Yu's notation, but I think I'm answering the right
question.  If two variables A and B form a bivariate normal distribution and
both variables have means of 0, then Var(A*B) = Var(A)*Var(B)*(1+r^2), where r
is the correlation between A and B.  In the present case we can set r = 0,
since he specifies A and B are independent.
Dick Darlington, Psychology, Cornell
#000#
From: ky04@ns1.cc.lehigh.edu (KUN YU)
Newsgroups: bit.listserv.stat-l
Subject: Variance of AxB?
Date: 1 Oct 92 21:10:20 GMT
Organization: Lehigh University

Suppose A and B are vectors of random numbers. Each has multivariate normal 
distribution with 0 mean.
 A(1,n), ~N(sigma1, 0), i.e., each element in A has variance sigma1, mean 0.
 B(n,1), ~N(sigma2, 0)

What is the variance of $A {\times} B$?

ps. I know if sigma2=0, var(AB)=B'B sigma1
-- 



                                                          _                  
ky04@lehigh.edu                                         / _ \
KYu, BioProcessing Institute                          /\ _|_ /\
111 Lehigh University                                    /|\
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X-Acknowledge-to:  XIC@NIHCU.BITNET
Newsgroups: bit.listserv.stat-l
Date:         Tue, 6 Oct 1992 09:49:10 EDT
From:         XIC@NIHCU.BITNET
Subject:      Use of SUDAAN on rare traits

This is in response to the query of Marcie-jo Krenow about the
use of SUDAAN to analyze survey data for rare traits.

In general, one needs at least 2 PSUs to be represented out of
each stratum from which they were selected.  There is a "fix"
built into SUDAAN to handle the situation where this does not
occur, though.  I have been told (by RTI personnel) that it is
better to collapse similar strata together (and treat the
result as a single stratum) rather than to rely on the SUDAAN
"fix".  No matter what you do, the variance estimates will not
be as stable (have fewer degrees of freedom) than in more
usual situations.

Note that if one is estimating the variance of counts (where 0
is a valid response) there is no real problem.

B. Shah at RTI might be able to provide a more definitive
answer.

from
Michael P. Cohen, National Center for Education Statistics
Washington, DC 20208-5654
Bitnet:  XIC@NIHCU    Internet:  XIC@CU.NIH.GOV
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Date:         Tue, 6 Oct 1992 11:42:32 EST
From:         "A. NARAYANAN DECISION & INFO. SYSTEMS--855-2665"
              <NARAYANA@UCS.INDIANA.EDU>
Subject:      SWEEP operator

>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>


Jim Goodnight of SAS Institute has done some work on this and I am sure
he will have an SAS program to do this. Check the following reference:

Goodnight, James H. (1978). The SWEEP Operator: Its Importance in
Statistical Computing, SAS Technical Report Series R-106, SAS Institute
Cary, NC.

If I remember right, he had published in The American Statistician on the
same topic sometime in the last 5-6 years.

Narayanan, A
Indiana University
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Date:         Mon, 5 Oct 1992 19:16:46 CDT
Comments:     "U. of Illinois at Chicago"
From:         Allen Herzog <U50161@UICVM.BITNET>
Subject:      Mallow's Cp statistics in SAS Proc Reg

         I have cross-posted this query to SAS-L.  However, since the
use of Cp for variable selection appears to be controversial.  I thought
I might also add this query to STAT-L. I would welcome any replies and
will summarize them.

 ---------Note sent to SAS-L------------------------------

         In using the CP selection criterion the SAS/STAT manual
states "The method is similar to ADJRSQ, except that Mallow's CP
statistic is used as the criterion for model selection."  In the
output, however, it is the unadjusted R-square that is printed.
Two questions.  First, although none of the textbook examples
show megative CP values, this often occurs.  Looking at equations this
seems very possible.  Second, an important matter in using this statistic
is having an unbiased estimate of sigma-square to calculate CP.  The
mean sqaure from the complete apparently serves this purpose. If
there are a large number of variables in the complete equation and
a relatively small number in the selected equation how does this
effect the estimates of CP.  Third, what is the effect if the
independent variables are almost orthogonal on CP for any particular
subset?   What is the effect on CP if the variables are highly collinear?
Is there any heuristic rule for selecting an unbiased set of varibles
for the complete set?
    I would appreciate any insight on these questions. References would
be particularly welcome.  I have looked at Daniel & Wood (Fitting Equations
to Data) and D.F. Morrison (Applied Linear Statistical Models) which I
found helpful. Morrison gives a particularly clear exposition.
    A caution is by R.B. Darlington is worth quoting.  He writes,
      "A method called all-subsets regression considers all possibilities.
A statistic called Mallows Cp is often used to choose the best of these
regressions. In my opinion there is no clear rationale for Cp..."(Regression
and Linear Models, McGraw-Hill, 1990, p. 166). He recommends a method due
to Browne which he calls "shrunken" R.
                                                  Allen Herzog
                                                  U50161@UICVM
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Date:         Wed, 30 Sep 1992 15:18:49 PDT
From:         Nino Pagliccia <pagli@UNIXG.UBC.CA>
Subject:      Re: Matching cases

In a similar situation as the one described by Brian Day
(selecting matching cases from a larger sample), I controlled
the matching cases by geographical location since it was
important for me that the matched cases came from the same communities.
Within each community I selected a random sample. This way
I did not have to worry about the "community effect".
--
NINO PAGLICCIA  	                         Ph. (604)822-5009
Centre for Health Services and Policy Research   FAX (604)822-5690
429 - 2194 Health Sciences Mall
University of British Columbia                 E-mail PAGLI@UNIXG.UBC.CA
Vancouver B.C. V6T 1Z3
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Date:         Tue, 6 Oct 1992 12:00:51 +1000
From:         alan@DMSMELB.MEL.DMS.CSIRO.AU
Subject:      Re: Mallow's Cp statistics in SAS Proc Reg
              05 Oct 92 19:16:46 CDT."
              <9210060030.AA02797@dmsmelb.mel.dms.CSIRO.AU>

1. Negative Cp.   I have never seen a negative value but it seems possible
   to obtain one.
2. You certainly should use an unbiased estimate of the residual variance.
3. Mallows Cp is a criterion for the case in which you have FIXED predictors,
   e.g. in an experimental design situation.   I know it is commonly used with
   random predictors but that is not the situation for which it was derived,
   and there are other estimates of mean squared errors of prediction for the
   case of random predictors.   There is a paper by Leo Breiman - sorry I
   don't have the reference handy - which shows that there can be big
   differences in the estimate of the MSEP between the fixed and random
   predictors situations when the number of predictors is nearly as large as
   the number of observations.
4. I don't understand what you mean by 'selecting an unbiased set of variables
   for the complete set'.   If you do any selection then you are probably
   introducing bias.
5. The properties of Mallows' Cp are for a subset of variables chosen either
   a priori or at random.   They do not apply if you choose the subset of each
   size with the smallest value of Cp.   It is very much like the use of
   F-to-enters in various forms of stepwise regression.   The distribution
   of an extreme variance ratio is very different from the F-distribution.
   Mallows looks at the properties of extreme Cp's for orthogonal predictors
   in his 1973 Technometrics paper.
6. Any situation in which you use the same data for model building and then
   for estimation gives biased estimates.   Typically, you can anticipate
   that regression coefficients will be of the order of one standard error
   too large, and your estimate of the residual variance for the chosen model
   will be far too small.
7. Orthogonality or high correlations amongst the predictors should not matter
   provided that good least-squares methods are used.
8. References.   I hope you will look at my book `Subset selection in
   regression' published by Chapman & Hall in 1990.   You won't find any
   cookbook recipes though for how to do it - just lots of warnings about
   the biases and an embryo maximum likelihood method for overcoming the biases
   due to over-fitting.

Alan Miller
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Date:         Thu, 1 Oct 1992 09:19:21 EDT
From:         Frank Dane <FDANE@UGA.BITNET>
Subject:      Re: Matching cases (fwd)
              30 Sep 1992 21:15:41 CDT from <phil@WUBIOS.WUSTL.EDU>

J. Philip Miller asked about the "problems}i" inherent in unequal
sample sizes.  At the very least, unequal ns increase the likelihood
of encountering heterogeneity of variance. (Of course, unequal ns
don't guarantee that condition.) When the disparity of the sample
sizes is >1 order of magnitude (e.g. 17 v. 3200), one is also likely
to run into the logical (not mathematical) problem of dealing with
an idiosyncratic sample (the smaller one) with respect to generali}izing
the results of any analyses.  I'm sure others on the list will note
other problems.

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:         Mon, 28 Sep 1992 07:53:00 PDT
From:         HOFER@USCMVSA.BITNET
Subject:      Choice of random number generators

What are some of the "best" algorithms for generating random
numbers? I have been performing simulations using number generators
such as GENRAW (part of the LISREL package), and those in MicroSoft
BASIC and MS Fortran. However, I'm not certain that I am these are
producing the "best" random number sequences that I can get.
So my requests of the List are:
  1) What are the best algorithms to use when generating data for
simulations.
  2) Are there programming techniques to optimize the random number
generators such as those found in MS Fortran (such as using a
sequence of random numbers to provide the seed for the next random
number generator and so on...).
  3) Are there some good books on the topic of computer statistical
simulations?
I greatly appreciate in advance any responses that I might get. I
do realize that this issue might already have been discussed and
in this case would welcome some directions as to where I might access
the archives.
Many thanks again,
Scott M. Hofer
Dept of Psychology
Univ. of Southern California
<HOFER@MVSA.USC.EDU>
From: jjmorris@gandalf.rutgers.edu (Joyce Morris)
Newsgroups: bit.listserv.stat-l
Subject: test for difference?
Date: 6 Oct 92 19:52:35 GMT
Organization: Rutgers Univ., New Brunswick, N.J.

A colleague and I are trying to decide how to determine whether the
crime rates in two different locales are significantly different.
I think part of our problem in deciding what test to use is that
we are not sure whether we are dealing with a sample or a population.
If we were dealing with health data our first inclination would be to
do a risk ratio, but the readers of the paper would have not idea what
we had done.  The other options that we have considered do not seem to
be correct but we are not sure why other than a gut feeling.

The crime rate is the number of crimes per 1000 population in the locale.

We are open to suggestion as to the best, legitimate way to determine whether
they are significantly different. An explanation of why would also be
helpful.

Thanks in advance.  I will summarize if there seem to be a divergence of
opinion.

Joyce Morris  jjmorris@gandalf.rutgers.edu
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Date:         Thu, 1 Oct 1992 10:00:31 GMT
From:         Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
Subject:      It's in the OTHER envelope!

Now hang on. You pick an envelope, any envelope. Now, the
expected gain from opening the other envelope is (2X + 0.5X)/2 = 1.25X.
But if you switch, then the OTHER envelope is the one you WERE
holding up till a minute ago. It now makes sense to switch
back (expected gain, 1.25X). And of course, it makes sense to
keep switching forever.

EUREKA! This is simply a variant of Murphy's law of queues
(the OTHER queue is the one that's moving faster) and it says "there's more
money in the OTHER envelope."

Watson: "Mmmm... Sounds like a citrus is a crowd. 1,5,4"
Holmes (distantly): "A lemon tree, My dear Watson"

ronan
From: reanier@milton.u.washington.edu (Richard Reanier)
Newsgroups: bit.listserv.stat-l
Subject: Archaeologist needs help
Date: 2 Oct 92 16:26:09 GMT
Organization: University of Washington, Seattle

Recently I completed an analysis of some archaeological materials
that led me to reconsider what I thought I knew about statistical
populations and samples.  As a nonstatistician I confess some
confusion about these issues, and being concerned that I am
"driving without a license" in these areas, I invite your
comments.

If one excavates an entire archaeological site and recovers all
of the artifacts, what exactly does one have; a population or a
sample?

One position is that one has a sample from the hypothetical
population of all archaeological sites that "could have been
made" by the prehistoric inhabitants.  A second position holds
this is nonsense; one has a population (Shennan 1988:58).

In my study the problem presents itself in the following way.  I
examined the spatial distributions of artifact types in an
ancient house and identified (by k-means clustering of grid
coordinates) eight spatial clusters of artifacts with varying
numbers of each artifact type in each cluster, which I chose to
further analyze.  Using a similarity measure and randomization
tests I sought to determine which clusters were significantly
different in content from one another.  The tests were pairwise
among all eight clusters (28 comparisons).

1. If I consider that the collection is a sample, then some
multiple comparison adjustment to alpha, such as the use of the
Bonferroni inequality, should be applied to the 28 comparisons.

2. If the collection is a population that has simply been
partitioned, then it seems to me there is no need for a multiple
comparison correction since there can be no sampling error
(except for the very small sampling error inherent in the large
number of Monte Carlo samples from the randomization
distribution).

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. 
Randomization tests in general are not used to generalize to a
target population, and have been used by reputable statisticians
in situations where "the concept of a population is irrelevant or
the data can be considered as representing the whole population"
(Manly 1991:3).

What are your thoughts on these matters?  I sense that they touch
upon some rather fundamental issues in statistics.  Reply to me
and I will summarize, or directly to the net if the topic is of
general interest, as you like.


Manly, Bryan F.J. (1991) Randomization and Monte Carlo Methods in
     Biology. Chapman and Hall, New York.

Shennan, Stephen. (1988) Quantifying Archaeology. Academic Press,
     San Diego.


Rick Reanier                    INTERNET reanier@u.washington.edu
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