HDTenEightyP/NetNews huggingface dataset:
From: charvey@teal.csn.org (Chip Harvey)
Subject: "Prestige" of Journals
Organization: Colorado SuperNet, Inc.
X-Newsreader: Tin 1.1 PL4
Date: Wed, 16 Dec 1992 18:23:04 GMT

Forgive me if this message is duplicated; I don't know what happened to my
first post.

I am seeking help in evaluating the "prestige" of statistical journals and
other journals publishing papers in statistics and ecological and
environmental statistics.

I am supposed to submit a ranking to a federal government panel of non-
statisticians for purposes of valuation of work published by government
researchers.  As is any such ranking, this too is a linear representation of
a multidimensional set of opinions and as such, is controversial.  The
criteria themselves are controversial.  Anyway, among statistical journals,
I anticipate differences based on theoretical/applied slants of the raters. 

The criteria which are to be used in evaluating "prestige" are: 1) degree of
difficulty in getting a paper accepted, 2) level of referee review, 3)
geographic extent of readership, 4) an assessment of the level of scientific
influence of the journal in the specific field (statistics, ecology,
environmental sciences), and 5) the extent to which senior scientists publish
their best papers in the journal.  There are four categories of "prestige"
ranging from "HIGHLY" to "LEAST."  The list of publications is open-ended.

Additionally, the panel is seeking ideas on how to rate the prestige of books,
book chapters, proceedings, etc.

I would appreciate any participation.  Please e-mail your lists, ratings, and
ideas to me, charvey@csn.org.  If there is general interest, I will post the
results.  I am cross-posting this request in bit.listserv.stat-l and
sci.math.stat.


Chip Harvey                                                 charvey@csn.org
National Park Service                                        (303) 969-2807
Air Quality Division					FAX: (303) 969-2822
Newsgroups: sci.math.stat
Subject: testing for linearity
From: mgranaas@charlie.usd.edu
Date: Tue, 15 Dec 1992 15:23:34 GMT
Reply-To: mgranaas@charlie.usd.edu
Organization: The University of South Dakota Computer Science Dept.

The simplest test for linearity that I have found is to calculate the
conditional means of y on x.  Follow that with plotting and calculating
the correlation coefficient.  If your correlation is near +/- 1 your
data are probably linear in form.  If not, the plot should show a fairly
clear picture of what shape the relation takes.  If the plot does not
show a fairly clear picture, then you do not have sufficient data at
each level of x to dampen out the error.  (This happens frequently, and
at that point you're back to square one--sorry)

Michael Granaas
Dept. of Psychology
University of South Dakota
mgranaas@charlie.usd.edu

How do I get a sig file into this post???
From: Russel_Mcauliffe@equinox.gen.nz (Russel Mcauliffe)
Subject: Simple Odds on dice.
Newsgroups: sci.math.stat
Summary: 
Keywords: 
X-Newsreader: TIN [version 1.1 PL8]
Date: 19 Dec 92 01:04:54 +1200
Organization: Equinox Networks

I have just been playing drinking games with a friend of mine and we had an
argument about the odds of wining [losing??!!], and I was wondering if
anyone could clarify the situation.

The game was a very simple one (you don't need to be at all sober to play)
and it basically goes like this:-

There are 3 die(dice). You call a number before throwing the die and however
many times that number comes up (out of the three die) you have to 'skull'
that many glasses. My friend reckoned that there was a 1 in 6 chance of
'skulling' but obviously that was completely wrong. No matter how I tried to
point out he was wrong, he wouldn't beleive me and as I was [am] quite drunk
I couldn't come up with a suitable arguement. What I would like to know is
the odds of throwing a single, a double and a triple of the number.

Any written proof in my argument would be much appreciated.
Cheers!!!!!
RJ.
From: feh@duke.edu (Frank Harrell)
Newsgroups: sci.math.stat
Subject: Re: stepwise regression
Summary: amen
Date: 18 Dec 92 02:15:30 GMT


I very much agree with Scott Thompson's thoughful post. More often
than not, the best prediction arises from a full model fit
(i.e. collinearities aren't really all that dangerous) followed
by shrinkage. Spiegelhalter (Stat in Med 5:421-33, 1986) says
this better than I could.

-- 
----------------------------------------------------------------------------
Frank E Harrell Jr			feh@biostat.mc.duke.edu
Associate Professor of Biostatistics
Division of Biometry                    Duke University Medical Center
Newsgroups: sci.math.stat
From: thompson@atlas.socsci.umn.edu (T. Scott Thompson)
Subject: Peter Hall's book on the bootstrap.
Reply-To: thompson@atlas.socsci.umn.edu
Organization: Economics Department, University of Minnesota
Date: Fri, 18 Dec 1992 20:44:06 GMT

I noticed a Springer-Verlag advertisement in a recent issue of AmStat
News for the book "The Bootstrap and Edgeworth Expansion" by Peter
Hall.  There was a little "splat" on top of the ad that read "Newly
Corrected Printing".  Since I had bought the book last winter when it
first came out I called Springer and asked for an errata sheet.  They
mailed me a new copy of the book instead, which arrived today.  (I
still don't know what, exactly, was wrong with the first printing.)

Curiously the little "splat" is gone from the latest ad in the
December issue.

I thought that some other statisticians might want to know about this.
--
T. Scott Thompson              email:  thompson@atlas.socsci.umn.edu
Department of Economics        phone:  (612) 625-0119
University of Minnesota        fax:    (612) 624-0209
From: chaas@coe.drexel.edu (Chuck Haas)
Newsgroups: sci.math.stat
Subject: Re: Robust chi-squared routine?
Date: 11 Dec 92 14:51:59 GMT
Organization: Drexel University
X-Xxmessage-Id: <A74E162A1B0118D7@n1-24-215-coe-abbotts.coe.drexel.edu>
X-Xxdate: Fri, 11 Dec 92 01:55:54 GMT
X-Useragent: Nuntius v1.1.1d13

Under conditions that you described, certainly except for the far tails,
the distribution should be reasonably well approximated by a normal
distribution.  Perhaps someone else has a reference to the numerical
conditions under which such approximation is "sufficiently close".




Charles N. Haas
Professor, Environmental Engineering
Drexel University, Philadelphia PA 19104
======================================
CHaas@COE.Drexel.Edu
From: len@mel.dit.csiro.au (Len Makin)
Newsgroups: sci.math.stat
Subject: Time series de-trend and df
Date: 21 Dec 92 01:39:32 GMT
Organization: CSIRO DIT (Melb.)


A Climate modeller colleague asked the following:
I am planning to remove trend from some GCM results (CO2 levels).
I suspect this affects degrees of freedom for t-tests, F-tests, etc.
Can  anyone help please?
I plan to remove the trend without changing the mean.
I suspect not changing mean implies that only one df is lost.

So if original data are 
y[1], y[2], ... , y[i], ... , y[N]
where i represents year, so data are equally spaced in time.

Detrended value for year i is:
y[i] - r(y,i) * ( sd(y) / sd(i) ) * ( i - mean(i) )

where r(y,i) is correlation between y & i
r(y,i) = ( sum(i*y[i]) - mean(i) * mean(y) ) / ( sd(i) * sd(y) )

Since i is simply 1, 2, ... , N
by algebra:
mean(i) = ( N + 1 ) / 2
sd(i) = sqrt( ( N * N - 1 ) / 12 )

In my case N = 20 for both control (normal CO2) & double CO2 samples.
I plan to detrend both samples before comparing 
(1) means using t-test 
(2) variances using F-test.

What is the effect of this detrending on degrees of freedom for these tests?
Are these tests meaningful?

Len.Makin@mel.dit.csiro.au               | Senior Computer Scientist    |
CSIRO Division of Information Technology | Supercomputing Support Group |
723 Swanston Street, Carlton             | Tel: +61 3 282 2622          |
VIC 3053, Australia                      | Fax: +61 3 282 2600          |
Newsgroups: sci.math.stat
From: R0264@vmcms.csuohio.edu
Subject: Re:Statistics packages for IBM pc
Organization: CSU
Date: Wed, 16 Dec 1992 17:49:13 GMT

You might try |STAT from Gary Perlman at the Wang Institute (as of 1986)
of Graduate Studies, Tyng Road, Tyngsboro, MA 01879.  I have no e-mail
adress.  A few years ago he asked only about $15 to cover mailing,
etc., but required a SASE mailer for floppy disks.  You'd best write
for info first.   It has a lot of data manipulation capabilities,
most standard simple stat routines, but almost no graphics.  He ported
it from his own UNIX version to MS-DOS.
                            Phil Emerson
                            Cleveland State University
 
 
In article <8755@lhdsy1.lahabra.chevron.com>
rcj@lhdsy1.lahabra.chevron.com (R.C. Jones) writes:
 
>
>I'm looking for a statistics/probability program for the IBM pc.
>Any reccomendations? How to order and approx. how much?
>Will be involved in an introductory college course in probability
>and statistics. Thanks for any advice.
>
>
>--
>  |___________________________________________________________________|
>>>| Dik Jones, Earth Model Interpretation   Internet: rcj@Chevron.com |
>  | Chevron Petroleum Technology Company.        Tel:  (310) 694-7056 |
>  | La Habra, California 90633-0446              Fax:  (310) 694-7063 |
Newsgroups: sci.math.stat
From: rivas@fraser.sfu.ca (Jorge David Rivas)
Subject: Re: Simple Odds on dice.
Organization: Simon Fraser University, Burnaby, B.C., Canada
Date: Sat, 19 Dec 1992 09:29:39 GMT

In article <168C1D231.R0264@vmcms.csuohio.edu> R0264@vmcms.csuohio.edu writes:
>Well, unless there is some trick that I don't see, P(just once) =
>(1/6)(5/6)(5/6); P(twice) = (1/6)(1/6)(5/6); P(thrice) = (1/6)(1/6)(1/6).
>These are mutually exclusive, so P(once or more) = 31/16.
>Phil, Cleveland State University.

NO PROBABILITY THAT I KNOW OF CAN BE GREATER THAN 1 !!.

Instead consider:

assuming your dice are fair we have p = 1/6 you get any number from ONE die.
Now, if you are using 3 dices the probability that comes with any combination
of outcomes of the THREE dices follows what is known as the binomial distribu-
tion, that is:
        n  x     n-x          n        n!
p(x) = ( )p (1-p)    , where ( ) = ----------
        x                     x     x!(n-x)!

p = 1/6 , n = number of trials (3 in this case), and x = number of posible out-
comes (in this case x=0, x=1, x=2, x=3)   x=0 => your number did not come up
                                          x=1 => your number came up once
                                          x=2 => your number came up twice
                                          x=3 => your number came up in all dice

if you work this up you get p(x=0) = 0.57870
                            p(x=1) = 0.34722
                            p(x=2) = 0.06944
                            p(x=3) = 0.00463
of course you might view these as percentages by multiplying by a hundred ( but
be careful with the interpretation!!)

If your friend is not satisfied with just the formula then work out the numbers
for, say, x=1. Then:
           1   5  2
p(x=1) = 3---(---)
           6   6

that is if one of the die has your number this happens with p=1/6 .Since your
number came up only ONCE the other two dice must have any of the other 5
possible outcomes with p= 5/6 each . Now, since we require that these two events
(one die has your number, the other two don't) happen simultaneously we must
multiply their probs. hence (1/6)(5/6)(5/6) the multiplier 3 is, of course,
because the event I mentioned could happen in three different ways (first with
die A, then with die B, and finally with die C).

The other probabilities are found using simmilar reasoning.
-- 
 jorge david rivas (rivas@sfu.ca)    __@ !!  /  "It has taken mankind a million
  undergrad (Math & Stats)         _(\<,_   /  years to alter irreversibly the
SIMON FRASER UNIV, BC. CANADA     (X)/ (X) / planet, something whales could not
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ do in ten times as long"  :(
From: ajayshah@almaak.usc.edu (Ajay Shah)
Newsgroups: sci.math.stat
Subject: Re: Estimation of spline join points? References, suggestions?
Date: 15 Dec 1992 14:25:33 -0800
Organization: University of Southern California, Los Angeles, CA
NNTP-Posting-Host: almaak.usc.edu
Keywords: Splines, join points, Maximum-Likelihood

gyan@unixg.ubc.ca (Gyan P. Sinha) writes:

>(1) OPTIMIZATION STRATEGY: ML estimation will require some type of
>non-gradient  technique (?), so how do I get the asymtotic t-stats and
>such? 

@article{TishlerZang,
	author = 	{Asher Tishler and Isreal Zang},
	title = 	{A Maximum Likelihood Method for Piecewise Regression Models with a Continuous Dependent Variable},
	journal = 	AS,
	volume = 	30,
	number = 	2,
	pages = 	{116-124},
	year = 		1981,
	annote = 	{MLE piecewise regression.  Approximates max operator in LF.  Speed.}
}

(This might come with source code).

@article{TishlerZang81,
	author = 	{Asher Tishler and Israel Zang},
	title = 	{A new maximum likelihood algorithm for piecewise regression},
	journal = 	JASA,
	volume = 	76,
	number = 	376,
	pages = 	{980-987},
	month = 	{December},
	year = 		1981,
	annote = 	{regression, splines, piecewise regression, switching regressions}
}

If you write some interesting source, could you post on the net?

>(2) MEANING OF T-STATS: If I am estimating the join point from the
>data itself, does that require some special tweaking on the t-ratios
>to get legitimate tests of significance?

Nothing comes to mind.

>(4) ALTERNATIVES: Can anyone suggest alternatives that would allow for
>similar effects, other than introducing a polynomial in DIFF, of which
>I'm already aware of.

A alternative to polynomials and splines is chebyshev expansion.
-- 
Ajay Shah, (213)749-8133, ajayshah@rcf.usc.edu
From: rcole@ccwf.cc.utexas.edu (Richard W. Cole)
Newsgroups: sci.math.stat
Subject: RE: Testing for Linearity
Date: 16 Dec 92 20:16:33 GMT
Reply-To: rcole@ccwf.cc.utexas.edu (Richard W. Cole)
Organization: The University of Texas at Austin, Austin TX
Originator: rcole@sylvester.cc.utexas.edu

>>Why don't you try calculating the correlation coefficient: the closer it
>>is to 1 or -1, the more linear your data.

>I do not think that the corr. coef. would tell much at all right away.    
>If he is asking about linearity I'm sure he has in mind fitting a straight 
>line.
>He says:  

>>" I have a question regarding, linearity
>>of data points.  Does anybody know of any statistical
>>test for linearity. I would appreciate any kind of
>>help, references etc."

>I would think that PLOTTING the data first would be necessary in order 
>to asses the rest of the test:

>ie:if there is a pattern other than some kind of random scattering along some
>invisible straight line (ie: curvilinear like a parabola) then he should fit
>a polynomial model (possibly of second order), then the reduced model with 
>only the linear term and THEN asses linearity by means of the corresponding 
>tests.
I agree.   Actually testing the correlation is already assuming that 
linearity exists (a constant change in y per unit change in x) and the 
correlation test is a hypothesis that the slope (constant change in y) 
is equal to zero.  Working with a model not assuming linearity (one with 
linear and  quadratic and cubic components) and then testing a linearity 
restriction would seem much more in order since linearity technically
can exist when the slope is equal to zero.  Once you've then assumed 
linearity you may want to test that the slope is zero (where the 
restricted model has only an intercept term as a predictor).  Accepting
this model might suggest that the functional form of y is more complex
than fitting any of the models thus far or that their is no relationship
at all.

========================================================
Richard Cole, Stat/Math Services         Learning to  
Computation Center UT Austin TX, 78712   Paint by 
rcole@ccwf.cc.utexas.edu                 Numbers 
========================================================
From: rick@daffodil.tymnet.com (Rick McFarland)
Newsgroups: sci.math.stat
Subject: Re: Test for Linearity ??
Date: 11 Dec 92 19:13:47 GMT
Organization: BT North America, San Jose, CA

Why don't you try calculating the correlation coefficient: the closer it
is to 1 or -1, the more linear your data.



------------------------------------------------
"ad astra per aspera"
Xref: sparky sci.math.stat:2657 sci.math.num-analysis:3648
From: pa@verano.sba.ca.us (Pierre Asselin)
Newsgroups: sci.math.stat,sci.math.num-analysis
Subject: Re: Double decorrelation
Date: 19 Dec 92 18:10:00 GMT
Followup-To: sci.math.stat
Organization: None.  Santa Barbara, CA

In <1gq7rgINNcqk@agate.berkeley.edu>
	shein@nima.berkeley.edu (Soren Hein) writes:

>Given a matrix X (M by N) of stochastic variables, find two matrices 
>A (M by M) and B (N by N) such that for the transformed matrix
>        Y = A X B,  
>we have
>        E[Y Y^T] = I_M
>        E[Y^T Y] = I_N

I'm not sure it can be done.  If you can settle for diagonal matrices
instead of identities, a singular value decomposition (essentially)
will do the job.  I assume you know the covariance matrix of your
Xij's.

1) Restrict A and B to orthogonal matrices.  Then,
   Y Y^T =  A X X^T A^T  independent of B, and
   Y^T Y =  B^T X^T X B  independent of A.

2) Form the deterministic matrix E[X X^T] and diagonalize.
   The eigenvectors are the columns of A.

3) Form the deterministic matrix E[X^T X] and diagonalize.
   The eigenvectors are the rows of B.
-- 

--Pierre Asselin, Santa Barbara, California
  pa@verano.sba.ca.us
Newsgroups: sci.math.stat
From: david@concave.cs.wits.ac.za (David Lubinsky)
Subject: Request for code for enumerating combinations
Organization: Computer Science, University of the Witwatersrand
Date: 14 Dec 92 11:09:50 GMT

Does anyone have any code that they would be willing to share that
steps through each possible combination of n choose m items.

I know this is not too hard to do recursivley, but am hoping to find
some efficient implementation, since I need to do this for quite large
n's and m's.

Thanks
David
-----------------------------------------------------------------------
David Lubinsky                              david@concave.cs.wits.ac.za
Dept. of Computer Science                         Phone: 27 11 716-3459
University of Witwatersrand                         FAX: 27 11 339-7965
Private Bag 3, Wits 2050                           Home: 27 11 646-2929
Johannesburg, South Africa

--
-----------------------------------------------------------------------
David Lubinsky                              david@concave.cs.wits.ac.za
Dept. of Computer Science                         Phone: 27 11 716-3459
University of Witwatersrand                         FAX: 27 11 339-7965
Newsgroups: sci.math.stat
From: R0264@vmcms.csuohio.edu
Subject: Re: Simple Odds on dice.
Organization: CSU
Date: Fri, 18 Dec 1992 19:56:32 GMT

In article <Russel_Mcauliffe.5w90@equinox.gen.nz>
Russel_Mcauliffe@equinox.gen.nz (Russel Mcauliffe) writes:
 
>
>I have just been playing drinking games with a friend of mine and we had an
>argument about the odds of wining [losing??!!], and I was wondering if
>anyone could clarify the situation.
>
>The game was a very simple one (you don't need to be at all sober to play)
>and it basically goes like this:-
>
>There are 3 die(dice). You call a number before throwing the die and however
>many times that number comes up (out of the three die) you have to 'skull'
>that many glasses. My friend reckoned that there was a 1 in 6 chance of
>'skulling' but obviously that was completely wrong. No matter how I tried to
>point out he was wrong, he wouldn't beleive me and as I was [am] quite drunk
>I couldn't come up with a suitable arguement. What I would like to know is
>the odds of throwing a single, a double and a triple of the number.
>
>Any written proof in my argument would be much appreciated.
>Cheers!!!!!
>RJ.
Well, unless there is some trick that I don't see, P(just once) =
(1/6)(5/6)(5/6); P(twice) = (1/6)(1/6)(5/6); P(thrice) = (1/6)(1/6)(1/6).
These are mutually exclusive, so P(once or more) = 31/16.
Phil, Cleveland State University.
Newsgroups: sci.math.stat
From: R0264@vmcms.csuohio.edu
Subject: Re: looking for article on loaded dice
Organization: CSU
Date: Wed, 16 Dec 1992 18:08:01 GMT

In article <1992Dec15.233405.2306@muddcs.claremont.edu>
jhoste@kuju.claremont.edu (Jim Hoste) writes:
 
>
>Next semester I will be teaching an elementary probability class
>structured around the theme of games and gambling. I want to include some
>material on loaded dice. Surely someone has worked out the physics of
>rolling a die (in the shape of a cube) whose center of mass is not in the
>center of the die. What are the probablities of landing on the various
>faces as a function of the location of the center of mass? The same
>question can be asked for dice in the shape of other polyhedron,
>homogenious or not.
>	I am also interested in the following related question:
>Can one create a die having n faces where the probability of landing on
>each of the faces is given in advance?  This is certainly true if *die* is
>interpreted fairly loosely. Somebody must have worked this out already.
>	Can anyone recommend any good articles on these topics?
>	I will summarize the replies that I receive.
>	Thanks, Jim Hoste
>
>ps I am also posting to:
>	rec.gambling
>	sci.math
>	sci.physics
See Letter to Editor re Kemp & Kemp, The American Statistician, August
1992, Vol. 46, No 3, pp. 239-240.  Says  spots on most dice are
countersunk, which throws the center of gravity off.  Also, since
6 is opposite to 1, 5 to 2, etc., there is a predictable bias.
                       Phil Emerson
                       Cleveland State University
From: cthombor@theory.lcs.mit.edu (Clark D. Thomborson)
Newsgroups: sci.math.stat
Subject: Re: Robust chi-squared routine?
Date: 18 Dec 92 17:26:58 GMT
Organization: MIT Lab for Computer Science


   From: mbk@gibbs.ucsd.edu (Matt Kennel)

   Is a chisquared test appropriate for a situation where the
   number of bins is very large, but the expected value per bin is
   quite a bit smaller than 1?

Funny you should ask.  I have an as-yet-unpublished manuscript on this
very subject (which arose for me in the context of testing the output
of various pseudorandom number generators), available by anonymous ftp
from theory.lcs.mit.edu, directory pub/cthombor/Mrandom.

The "short answer" to your question is that the Pearson statistic is
useful for testing goodness-of-fit to a symmetric multinomial if n >
3\sqrt{k}, but you can't use the standard chi-squared tables safely in
this range.  The "discretization errors" are large, even near the
mean, unless (by the rule of thumb given in most textbooks)
      n > 5k
and (as is not disclosed in any textbook presentation, to my knowledge)
      k > 5
On the extreme upper tail, you're in trouble for any n and k.  The
non-symmetric case looks hopelessly complicated for any approach other
than "enumeration" of the relevant terms in the multinomial.
Furthermore, if there are large variations in cell probabilities, then
the Pearson statistic is pretty awful.  As long as you're enumerating,
you might as well just calculate the exact tail probability....

I still haven't figured out where to publish this stuff.  It was
rejected by the SODA conference.  Now I'm thinking about Interface
'93.  However, this conference seems to be dominated by applied
statisticians who want to learn how to use computers more effectively.
I don't think any mathematical statisticians are likely to attend, but
then again, I've not yet found any mathematical statistician who is
willing to think about "fixing up" that horrid old Pearson test.  The
usual response is that I should think about the likelihood ratio
statistic if I (shudder) really am sure I want to do a hypothesis
test.  In response, I've been digging into the Bayesian-frequentist
controversy, and I think I have a novel "compromise" between these
styles of reasoning.  If you're interested in getting a draft of this
compromise paper, please send email.

I'd appreciate any suggestions as to

1.  where next to submit my Pearson "fixup" result
2.  where to submit a paper on a Bayesian-frequentist compromise

To save net bandwidth, please respond by email.

							Clark

--
							Clark
Newsgroups: sci.math.stat