HDTenEightyP/NetNews huggingface dataset:
Organization: TECHNION - Israel Institute of Technology.
Date: Friday, 8 Jan 1993 09:00:51 IST
From: <IERVA01@TECHNION.BITNET>
Newsgroups: sci.math.stat,sci.econ
Subject: Deterministic factorial analysis

Alexander Vaninsky, D.Sc.
Fac. Industrial Engineering, Technion, Haifa, Israel
Taanach St., 40/36, Haifa 32161,Israel
Fax: (972-4)-235-194 ,Tel. (972-4)-292-851
Email: ierva01@techunix.technion.ac.il.bitnet

     I am interested in setting up contacts with mathematicians,
statisticians and economists that are interested in providing
factorial analysis of economic systems using deterministic
approach and correspondingly relatively small amounts of data.
     Mathematical statement  of  the  problem  is  the  following.
State of economic system is described as a system of equations
F(X)=0, where X is a vector, and F, vector function. Particularly
these equations may be regression ones. Objective function Z = f(X)
is supposed to be given and the system is supposed to move from
initial to final state along curve L due equations of dynamics. The
curve L may be the optimal trajectory or may correspond to economic
hypothesis on dynamics of Xi-es.
     The problem that is solved is decomposition of increase of Z
into components corresponding to every of elements Xi.
    Supposing Z equal consequently to Xi i=1...n, we obtain
decomposition of every of them into components conditioned by every
of other Xi-s. If L is optimal trajectory we provide post optimized
analysis.
     Mathematical contents of this problem  derives from  necessity
of special axiomatic system for obtaining of substantial solution.
This solution in its turn have many interesting  and important
applications in practice. Particularly, I have positive experience
of its usage in project evaluation, in developing of business games
for training of managers and in lecturing courses in the Moscow
State University.
From: baumgart@masg1.epfl.ch (Marcel Baumgartner)
Newsgroups: sci.math.stat
Subject: Hosmer, Lemeshow: looking for datasets
Date: 4 Jan 93 13:52:38 GMT
Organization: Ecole Polytechnique Federale de Lausanne

Hello,

Could anyone get me the datasets from the Wiley Book (1989)
"Applied Logistic Regression" by D. W. Hosmer and S. Lemeshow
by e-mail? They aren't on statlib.

Thanks in advance!   Marcel  (baumgart@masg6.epfl.ch)

Newsgroups: sci.math.stat
From: dmurdoch@mast.queensu.ca (Duncan Murdoch)
Subject: Re: Nonrandom Random Numbers?
Organization: Queen's University
Date: Wed, 13 Jan 1993 03:42:46 GMT

In article <1993Jan13.002020.10338@Princeton.EDU> rdnelson@phoenix.Princeton.EDU (Roger D. Nelson) writes:
>>In article <dmurdoch.393.726873235@mast.queensu.ca> dmurdoch@mast.queensu.ca
>(Duncan Murdoch) writes:
>>>The paper is Ferrenberg, A. M., Landau, D.P., and Wong, Y.J. (1992), Monte 
>>>Carlo simulations:  hidden errors from "good" random number generators,
>>>Physical Review Letters 69 no. 23, 3382-3384.
>>
>>Oops, I forgot to mention:
>>
>>The authors did find that simple variations on the algorithms (e.g. only use 
>>every 5th random number) gave acceptable results.

>I must be missing something, for _this_ result suggest high-frequency
>autocorrelations that are eliminated by dumping the 2-4th elements.

I suspect something like this:  the Ising model simulations probably have a 
very strong periodicity in how they use the random numbers.  That "strains" 
the RNG at one particular frequency in its spectrum.  Something about the 
Wolff algorithm is very sensitive to any deviation from the white spectrum 
at that particular frequency.  The CONG generator just happened to do best 
at that frequency; the others were off by a bit.  When the Wolff algorithm 
amplified the problems, they showed up as errors in the mean in the 4th 
or 5th decimal place.

The "every 5th" solution was presented as though it was one of many things 
the authors tried (though I don't think they said this).  It may well just 
be chance that using every 5th number happens to bring the right spectral 
density to that critical spot.

I think they got the conclusions bang on:  you can't rely on standard tests 
to validate a RNG when you're going to use it in ten million repeated 
simulations.

Duncan Murdoch
dmurdoch@mast.queensu.ca
From: jerry@hnrc.tufts.edu (Jerry Dallal)
Newsgroups: sci.math.stat
Subject: Want to buy:  ANCOVA by Huitema
Date: 8 Jan 93 14:16:02 GMT
Organization: USDA HNRC at Tufts University

For the last couple of years, I've been looking through the used book stores
for a copy of Huitema's Analysis of Covariance (Wiley).  Does any one have/know
of c copy for sale?  (I'd be even happier for a new one, but it's out of
print.)

Thanks much.
Xref: sparky sci.math.stat:2709 sci.econ:9586
Organization: TECHNION - Israel Institute of Technology.
Date: Wednesday, 6 Jan 1993 14:13:03 IST
From: <IERVA01@TECHNION.BITNET>
Newsgroups: sci.math.stat,sci.econ
Subject:    factorial analysis

Alexander Vaninsky, D.Sc.
Fac. of Industrial Engineering
Technion, Haifa,Israel
32161 Haifa, Israel
Fax: (972-4)-235-194
Tel. (972-4)-29-28-51(w)
Email: ierva01@techunix.technion.ac.il.bitnet
                                                       6.01.93

                        Dear Sir/Madam

     I am interested in setting up contacts with mathematicians,
statisticians and economists that are interested in providing
factorial analysis of economic systems using deterministic
approach and correspondingly relatively small amounts of data.
     Mathematical statement  of  the  problem  is  the  following.
State of economic system is described as a system of equations
F(X)=0, where X is a vector, and F, a vector function. Particularly
these equations may be regressions. Objective function Z = f(X)
is supposed to be given and the system is supposed to move from
initial to final state along curve L due equations of dynamics. The
curve L may be the optimal trajectory or may correspond to economic
hypothesis on dynamics of Xi-es.
     The problem that is solved is decomposition of increase of Z
into components corresponding to every of elements Xi.
    Supposing Z equal consequently to Xi i=1...n, we obtain
decomposition of every of them into components conditioned by every
of other Xi-s. If L is optimal trajectory we provide post optimized
analysis.
     Mathematical contents of this problem  derives from  necessity
of special axiomatic system for obtaining of substantial solution.
This solution in its turn have many interesting  and important
applications in practice. Particularly, I have positive experience
of its usage in project evaluation, in developing of business games
for training of managers and in lecturing courses in the Moscow
State University.
     I should be very interested in setting up contacts with colleagues
working in similar directions.

                  Sincerely yours

                                              A.Vaninsky,
                                              MS Math., D.Sc.Econ.
Organization: TECHNION - Israel Institute of Technology.
Date: Wednesday, 6 Jan 1993 17:03:39 IST
From: <IERVA01@TECHNION.BITNET>
Newsgroups: sci.math.stat
Subject: Deterministic factorial analysis

Alexander Vaninsky, D.Sc.
Fac. of Industrial Engineering
Technion, Haifa,Israel
32161 Haifa, Israel
Fax: (972-4)-235-194
Tel. (972-4)-29-28-51(w)
Email: ierva01@techunix.technion.ac.il.bitnet

     I am interested in setting up contacts with mathematicians,
statisticians and economists that are interested in providing
factorial analysis of economic systems using deterministic
approach and correspondingly relatively small amounts of data.
     Mathematical statement  of  the  problem  is  the  following.
State of economic system is described as a system of equations
F(X)=0, where X is a vector, and F, vector function. Particularly
these equations may be regression ones. Objective function Z = f(X)
is supposed to be given and the system is supposed to move from
initial to final state along curve L due equations of dynamics. The
curve L may be the optimal trajectory or may correspond to economic
hypothesis on dynamics of Xi-es.
     The problem that is solved is decomposition of increase of Z
into components corresponding to every of elements Xi.
    Supposing Z equal consequently to Xi i=1...n, we obtain
decomposition of every of them into components conditioned by every
of other Xi-s. If L is optimal trajectory we provide post optimized
analysis.
     Mathematical contents of this problem  derives from  necessity
of special axiomatic system for obtaining of substantial solution.
This solution in its turn have many interesting  and important
applications in practice. Particularly, I have positive experience
of its usage in project evaluation, in developing of business games
for training of managers and in lecturing courses in the Moscow
State University.

Newsgroups: sci.math.stat
From: jja@fnbc.com (Joseph Alotta)
Subject: Re: Recommendation please: basic curve fitting software
Organization: First National Bank Of Chicago, Chicago IL, USA
Distribution: sci.math.stat
Date: Thu, 7 Jan 93 20:38:59 GMT

In article <REYNOLDS.93Jan6142416@park.bu.edu> reynolds@park.bu.edu (John  
Reynolds) writes:
> 
> A friend from the psychology department asked me if I could recommend
> curve fitting software and I told him I'd ask around.  He wants
> something that's not too complicated for either the PC or his UNIX
> account at school, and all he wants to do is to use it to do 1-D least
> squares fits.  He says he has "a lot" of data, so nothing that gets
> overloaded by large data sets.  Does anything come to mind?  Please
> reply by email.
> 
> Thank you. -John

i've messed around with FFIT a shareware program that
meets your specs rather nicely.  costs all of $3.19.

call software labs, (310) 410-2030 ask for
FFIT disk number 7606.


joe.

--
==============================================================
! joe alotta                                    jja@fnbc.com !                                               
!                                             (312) 732-3439 !
!                                                            !
! "The main thing is to keep the main thing the main thing." !
! Dr. George Sweeney                                         !
==============================================================
Newsgroups: sci.math.stat
From: iwelch@agsm.ucla.edu (Ivo Welch)
Subject: S: lm() problem.
Organization: UCLA, Anderson Graduate School Of Management
Date: 12 Jan 93 10:22:40 PST


[S on NeXT; an April 1992 version; NeXTStep 3.0; 32MB RAM; ~40-80MB free disk]

I am having a serious problem with lm()'s use of disk space. I have a panel
data set with 1,109 observations and 37 variables, or about 164K in float
storage. One of these variables is a categorical factor, taking about 400
values (firm id's). A rough computation tells me that I would need a maximum
of 1MB (double) space to store one copy of the X'X matrix in full form.

> result <- lm(formula.complete, can, subset= (date<breakdate), singular.ok=TRUE)
Error in data[1:ll] <- old: Cannot allocate 39516128 bytes:
        options("object.size") is 20000000

Now this is a bit too much for my somewhat limited computer. (After I allow
more 40MB as object.size, I get a swapspace overflow.) How can lm() be so
voracious? Is there any way to tell it to be a bit more graceful about its
memory appetite? (I need to move to about 1,000 variables and 2,000 obs at
some point.) Obviously, rewriting lm() (for an S beginner like myself) is very
undesirable.

Please respond to me. I will summarize any helpful suggestions.

/ivo

PS: This is why I cannot do my problem in S. I will post a question on SAS,
where I also tried to solve my problem soon.
From: rizzo@cbnewsf.cb.att.com (anthony.r.rizzo)
Newsgroups: sci.math.stat
Subject: factorial design:  Resolution?
Date: 12 Jan 93 17:27:26 GMT
Distribution: na
Organization: AT&T

I'm sure that this is a trivial question for most of you.
But the answer isn't obvious to me.  So, here goes:

I'm doing a factorial experiment, 6 variables at two settings.
The variables are qualitative, not quantitative.
According to Box, Hunter, and Hunter, this is a 2^(6-1) half fraction,
because I'm using 32 runs (on a computer).  I've assigned
the 6th variable to the column for the 12345 interaction.
Does this make it a resolution VI design?  The generator
is:         I = 1 2 3 4 5 6  

so that     6 = 1 2 3 4 5
            1 = 2 3 4 5 6
            2 = 1 3 4 5 6
            3 = 1 2 4 5 6
            4 = 1 2 3 5 6
       and  5 = 1 2 3 4 6

Box, Hunter, and Hunter says that "In general, the RESOLUTION of a
two-level fractional design is the length of the shortest
word in the defining relation."  Am I interpreting matters correctly?

Tony Rizzo (att.com!hogpb!rizzo)
Newsgroups: sci.math.stat
From: rdnelson@phoenix.Princeton.EDU (Roger D. Nelson)
Subject: Re: Nonrandom Random Numbers?
Originator: news@nimaster
Organization: Princeton University
Date: Wed, 13 Jan 1993 00:20:20 GMT

In article <dmurdoch.396.726875230@mast.queensu.ca> dmurdoch@mast.queensu.ca (Duncan Murdoch) writes:
>In article <dmurdoch.393.726873235@mast.queensu.ca> dmurdoch@mast.queensu.ca (Duncan Murdoch) writes:
>>The paper is Ferrenberg, A. M., Landau, D.P., and Wong, Y.J. (1992), Monte 
>>Carlo simulations:  hidden errors from "good" random number generators,
>>Physical Review Letters 69 no. 23, 3382-3384.
>
>Oops, I forgot to mention:
>
>The authors did find that simple variations on the algorithms (e.g. only use 
>every 5th random number) gave acceptable results.
>
>Duncan Murdoch
>dmurdoch@mast.queensu.ca

I must be missing something, for _this_ result suggest high-frequency
autocorrelations that are eliminated by dumping the 2-4th elements.

Roger
From: wbdst+@pitt.edu (William  B Dwinnell)
Newsgroups: sci.math.stat
Subject: Re: Nonrandom Random Numbers?
Date: 13 Jan 93 04:29:07 GMT
Organization: University of Pittsburgh


Welp, "Numerical Recipes" warns about this stuff, and so did Knithxxx Knuth.
I just wonder how RAN3, from "Numerical Recipes" would hold up...
Newsgroups: sci.math.stat
From: thisted@galton.uchicago.edu (Ronald A. Thisted)
Subject: Re: Books On The Foundations Of Statistics?
Organization: Dept of Statistics, Univ of Chicago
Date: Sat, 9 Jan 1993 16:48:35 GMT

In article <1993Jan8.195334.489@magnus.acs.ohio-state.edu> regeorge@magnus.acs.ohio-state.edu (Robert E George) writes:
>"The Foundations of Statistics" by Leonard "Jimmie" Savage
>(possibly still in Dover paperback edition?)

One of the best articles on the subject is also (posthumously) by
Savage:  "On rereading R. A. Fisher" with commentary, 1976 Annals of
Statistics, pp 441- 483.



Ron Thisted
Department of Statistics/The University of Chicago
thisted@galton.uchicago.edu
From: flim@weber.ucsd.edu (Francis Lim)
Newsgroups: sci.math.stat
Subject: ACE (Alternating Conditional Expectations)
Date: 5 Jan 1993 04:09:34 GMT
Organization: University of California, San Diego
NNTP-Posting-Host: weber.ucsd.edu

Hello netters. I have a simple ACE (Breiman and Friedman 1985) question.

ACE stands for Alternating Conditional Expectations, and is a method
of estimating the relationship between the explanatory and response
variables, transforming the variables to maximize their correlation.

The presumed relationship between Y and X, for example, is

	\theta(Y) = \phi(X) + e

where e is random error.

One can use either BLSS or S to invoke ACE.
For both statistical packages, I found that \hat{\theta}(Y) did NOT
equal \hat{\phi}(X), where \hat symbolizes the estimated
transformations.

In fact, \hat{\theta}(Y) was not even proportional to \hat{\phi}(X).

Any ideas? Thanks in advance. Replies will be summarized and posted
to the net.
-- 
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
      FRANCIS W. LIM  |  email: flim@ucsd.edu | dept phone: (619) 534-3383
      Dept of Economics, Univ of Cal at San Diego, La Jolla, CA 92093-0508
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Newsgroups: sci.math.stat
From: jonathan@psych.psy.uq.oz.au (Jonathan Dwyer)
Subject: Re: Books On The Foundations Of Statistics?
Followup-To: sci.math.stat
Organization: University of Queensland
Date: Mon, 11 Jan 1993 11:30:52 GMT

Hi,

This is the best book I have found on the foundations of inductive thought
and probability as used today  (in fact I thought it was so good I bought
my own copy!) :

BEGIN INCLUDE==========================

   Type of Material: Book

     LC Call Number: QA276.15 .S75 1986

             Author: Stigler, Stephen M.

              Title: The history of statistics : the measurement of uncertainty
                      before 1900 / Stephen M. Stigler.

   Publication Info: Cambridge, Mass. : Belknap Press of Harvard University
                      Press, 1986.
  Phys. Description: xvi, 410 p. : ill. ; 24 cm.

              Notes: Bibliography: p. 374-398.
              Notes: Includes index.
           Subjects: Statistics--History.

     LC Card Number:    85030499
               ISBN: 0-674-40340-1 (alk. paper)


==END INCLUDE==========================


Also, I have not read this one, but it came up when I tried to find Stigler:

BEGIN INCLUDE==========================

   Type of Material: Book

     LC Call Number: QA276.15 .P44 1978

             Author: Pearson, Karl, 1857-1936.

              Title: The history of statistics in the 17th and 18th centuries
                      against the changing background of intellectual,
                      scientific, and religious thought : lectures by Karl
                      Pearson given at University College, London, during the
                      academic sessions, 1921-1933 / edited by E. S. Pearson.

   Publication Info: London : C. Griffin, c1978.
  Phys. Description: xix, 744 p. : ill. ; 26 cm.


              Notes: Bibliography: p. 732-734.
              Notes: Includes index.

           Subjects: Mathematical statistics--History--17th century--Addresses,
                      essays, lectures.
           Subjects: Mathematical statistics--History--18th century--Addresses,
                      essays, lectures.

        Other Names: Pearson, E. S. (Egon Sharpe), 1895-

     LC Card Number:    79318701 //r85
               ISBN: 0-85264-250-4 :  7.50


==END INCLUDE==========================


Newsgroups: sci.math.stat
From: mppu3@syma.sussex.ac.uk (Conor McMenamin)
Subject: Minimization for non-linear LSQ
Organization: University of Sussex
X-Newsreader: Tin 1.1 PL5
Date: Thu, 7 Jan 1993 11:23:13 GMT

Hi,
	I hope this isn't the wrong place to post. I am working on a
project where I have to do non-linear least-squares fitting with large
data sets to a complicated function. Up until now I have been using the
Downhill Simplex Method of Nelder and Mead (see Numerical Recipes) to
perform the minimization. This has given me good results, but is
extremely processor-time intensive. I have tried using Powell's method
(also see Numerical Recipes) but found that the Downhill Simplex is
better at finding deeper 'global' minima-Powell's tends to find a stable
local minimum and stay there. However, you may have noticed my problem,
I only have Numerical Recipes as a source. Does anyone have any
suggestions of references which will give a selection of minimization
techniques I can try which are a little more state-of-the-art than those
in NR? I am presuming that minimization techniques have emerged
which are not mentioned in NR which may be suitable.

Yours Hopefully,
Conor.

P.S. I'm only an experimental physicist, so please be gentle!
Newsgroups: sci.math.stat
Subject: quantile plots
From: wsimpson@uwpg02.uwinnipeg.ca
Date: 4 JAN 93 17:43:32 CST
Organization: University of Winnipeg

I am wondering about the statistical treatment of quantile plots.
Suppose we have a quantile plot that looks linear.  This reveals
a uniform distribution.

My question is this: how can we fit a line to the points?
Linear regression won't do since the observations in a quantile
plot are not independent.  I am particularly interested in
estimating parameters of the distribution from the best fitting line
and finding confidence intervals.

I am not especially interested in the particular example of uniform
distribution.  I am interested in the general problem of fitting
curves to quantile plots.

Thanks very much for any help.

Bill Simpson
From: glgyhrb@otago.ac.nz (Hyram Ballard)
Newsgroups: sci.math.stat
Subject: Help: Eigen routines for spreadsheet
Date: 13 Jan 93 10:59:21 +1300
Organization: Dept. of Geology, Univ. of Otago, NZ

Please accept my humble incursion into this group but I could use some help on
extracting eigenvalues and eigenvectors from symmetric matrices using a
spreadsheet. I have produced a multiple discriminant analysis model in Excel
which calculates the variance sum of squares matrices but to complete the
analysis I have to take the matrix into MatLabII to get the eigens. Although
this is no real problem it is a bit in-elegant and I would like to be able to
do the whole analysis within one application. If you have or know of any
spreadsheet routines or references to procedures suited to a spreadsheet
environment I would be very grateful if you could pass them on or point me in
the right direction.
Thank you for your help,
Hyram Ballard
Newsgroups: sci.math.stat,bit.listserv.stat-l
From: jerry@hnrc.tufts.edu (Jerry Dallal)
Newsgroups: sci.math.stat
Subject: Re: Nonrandom Random Numbers?
Date: 12 Jan 93 19:01:14 GMT
Organization: USDA HNRC at Tufts University

In article <1993Jan12.132319.6437@cbis.ece.drexel.edu>, chaas@coe.drexel.edu (Chuck Haas) writes:
> There was an article in today's (1/12/93) New York Times (page 1, section
> C) about some IBM researchers who found deficiencies in "standard" random
> number generators when using some crystallographic type of test.  Anyone
> on the net who would care to add details?  

For those who do not have the Times, the article says, " . . . the scientists
showed that five of the most popular computer programs for generating streams
of random numbers produced errors when they were used in a simple mathematical
model of the behavior of atoms in a magnetic crystal."  

Unfortunately (and understandably) the article does not identify the 5 
algorithms.  However, the article cites the original report from Ferrenberg et
al. in the Dec. 7 issue of Physical Review Letters.  I would be grateful if
someone with access to PRL would post the five generators.
From: reynolds@park.bu.edu (John Reynolds)
Newsgroups: sci.math.stat
Subject: Recommendation please: basic curve fitting software
Date: 6 Jan 93 19:24:16 GMT
Distribution: sci.math.stat
Organization: Boston University Center for Adaptive Systems


A friend from the psychology department asked me if I could recommend
curve fitting software and I told him I'd ask around.  He wants
something that's not too complicated for either the PC or his UNIX
account at school, and all he wants to do is to use it to do 1-D least
squares fits.  He says he has "a lot" of data, so nothing that gets
overloaded by large data sets.  Does anything come to mind?  Please
reply by email.

Thank you. -John
Newsgroups: sci.math.stat