HDTenEightyP/NetNews huggingface dataset:
Organization: TECHNION - Israel Institute of Technology.
Date: Wednesday, 6 Jan 1993 17:23:56 IST
From: <IERVA01@TECHNION.BITNET>
Newsgroups: sci.math.stat,sci.econ
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.
From: ph@physiology.oxford.ac.uk (Patrick Haggard)
Newsgroups: sci.math.stat
Subject: More on: testing for unequal variances of unrelated samples
Date: 7 Jan 93 17:59:36 GMT
Organization: Physiology Department, Oxford University, Oxford, UK.
Originator: ph@motor.physiol


Hello.  A few days ago I asked for information about how to test whether
two unrelated samples had different variances, and received a number
of helpful pointers from folks.  I would recommend the chapter in
Hollander and Wolfe as a useful place to start, if anyone else is
interested.

From a layman's viewpoint, an important criterion seems to be how
many assumptions you are prepared to make about the distribution.
If you can't make any, Moses test seems to be indicated.  If you're
happy to make some (asymptotic) assumptions, then Miller's jacknife
seems useful.  Unfortunately, the tests mentioned by Hollander and
Wolfe are not implemented in most of the standard packages.

Since my own data seems to be near normally distributed (both
on a priori and a posteriori grounds), I thought I might 
persevere with the traditional F test for variances, even though
this is known to be very sensitive to non-normality.  In particular,
I have been looking at Box and Andersen's adjustment to this test
(JRSS, B, 1955, v 17, p1-26).  They adjust the degrees of freedom of
the traditional F test for variances by a factor which depends on the
normality of the distribution.  I'ld like to make this adjustment to
my own data, but, as a non-statistician, I'm having a bit of trouble
understanding their notation.  They give the modfication factor
to the degrees of freedom as (1 + 1/2*gamma_2)^-1 (their equation 48,
on p. 17).  My question: what is gamma_2?  It looks as though it is
the kurtosis of both samples taken together, minus 3.  That certainly
fits with what Box and Andersen say later on p. 17.  Can anyone 
confirm that, or make any other relevant comments about adjusting
the degrees of freedom in such cases?

Thankyou.

--------------------------------------------------------------------------
Patrick Haggard                         Email (WORLD): ph@physiol.ox.ac.uk
University Laboratory of Physiology     Email (JANET): ph@uk.ac.ox.physiol
Parks Road,                             Tel. (0865) 272116
Oxford, OX1 3PT                         Fax. (0865) 272469
England
--------------------------------------------------------------------------
Newsgroups: sci.math.stat
From: vms@jyu.fi (Veli Matti Suppola)
Subject: Robust correlation coefficient
Keywords: robust
Organization: University of Jyvaskyla, Finland
Date: Sat, 9 Jan 1993 03:15:00 GMT

Hi!

I'm interested in robust correlation coefficients.

I will examine traditional correlation coefficients (Pearson,
Spearman, Kendall). These correlation coefficients are
sufficiently robust against a substantial number of outliers.
That is, they do not have high breakdown points.

I will try to find new correlation coefficients that have higher
breakdown points. I have found two 'new' coefficient but the more
the better. I will compare these coefficients through simulation
and try to find the best.

Robust correlation coefficients I have found:
   *Least Median of Squares (Abdullah 1990)
   *The Greatest Correlation Coefficient (Gideon 1987)

I don't have enough material to examine different robust
correlation coefficients. So, if You know some elementary
books about this area, please let me know. All types of
articles about this area are very welcome.

The second problem is that there are no programs, algorithms 
or statistical packages that can create robust correlation
coefficients. So I have to make these programs by myself. If
You know any programs or algorithms about this area, please
let me know.

Veli-Matti Suppola   vms@jyu.fi


Newsgroups: sci.math.stat
From: rizzo@cbnewsf.cb.att.com (anthony.r.rizzo)
Subject: Milliken experiment
Organization: AT&T
Date: Mon, 4 Jan 1993 22:15:04 GMT

When I was in college, the following experiment was among those 
required of us:  with the use of a watch, a microscope and two fiducial
lines on the lens, we had to time the descent rate of minute
particles of glass (microspheres).  The goal was to determine the
charge of a single electron from the speed measurements as the
spheres traversed a known distance in a known electric field.
In the absence of the electric field, the motion of the spheres
was somewhat brownian, with a slight bias for gravity.  But in
the presence of the electric field, the spheres reached a terminal
velocity that was a function of the number of charged particles
trapped on each sphere.  

For the sake of this discussion, the physics of the experiment
are irrelevant.  What is relevant is the technique by which
Milliken estimated the charge of a single electron.  The experiment
yields a wide distribution of velocities.  If all the spheres
had a single electron trapped upon then, then the distribution
would be centered about the velocity that corresponded to the
charge of one electron.  But there is no way of knowing how many
electrons are trapped on any sphere.  So, Milliken (and I ;-) 
collected many data points and tried to determine graphically,
by plotting a histogram, the charge of a single electron.
The lowest observed peak corresponded to the charge of a single
electron, and the other observed peaks corresponded to multiples
of the lowest, approximately.

Needless to say, Milliken was far more successful with this experiment
than was I.  But the question has come up again, in a slightly different
form.  Given a sequential listing, what algorithm can I use (program) to
determine the various means (or peaks), probability density functions,
and appropriate summary statistics?  I can spot the peaks manually,
of course, by simply plotting histograms and looking at them.  But I
would like to automate the process, since I might have to study
hundreds of listings.  References are most welcome, of course.
Thanks.

Tony Rizzo  (att.com!hogpb!rizzo)

(greetings Art.  How's the weather in CA? ;-)
From: thompson@atlas.socsci.umn.edu (T. Scott Thompson)
Newsgroups: sci.math.stat
Subject: Re: quantile plots
Date: 6 Jan 93 16:34:17 GMT
Reply-To: thompson@atlas.socsci.umn.edu
Organization: Economics Department, University of Minnesota

wsimpson@uwpg02.uwinnipeg.ca writes:

>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.

The quantiles of a distribution completely determine the distribution
and vice versa.  Likewise, a complete set of sample quantiles is
essentially a list of the order statistics of the sample, and is
equivalent to the empirical distribution function.

So your question is essentially equivalent to "How can we fit a
probability model to a data sample?"  It is hard to think of a broader
question within the field of statistics!  You will have to be much
more specific about what are the objectives of your analysis and what
else you know about the problem before you can have any hope of
obtaining a reasonable, non-trivial answer to your question.

If you have a parametric probability model in mind (the uniform model,
for example) then the problem may be solvable (depending on how
complete a set of quantiles are available) using maximum likelihood
methods.  The specifics depend on the probability model assumed.

You say that you want to "fit a line" to the quantile points.  This is
only a reasonable procedure if the data are known to have a uniform
distribution.  The true quantile plot for all other distributions will
be nonlinear.  On the other hand, if you know that the distribution
really is uniform, then the maximum likelihood estimate of the true
quantile plot is the line connecting the minimum and maximum observed
quantile points.
--
T. Scott Thompson              email:  thompson@atlas.socsci.umn.edu
Department of Economics        phone:  (612) 625-0119
University of Minnesota        fax:    (612) 624-0209
From: flim@weber.ucsd.edu (Francis Lim)
Newsgroups: sci.math.stat
Subject: Follow-up: ACE and Splus, why transformations don't sum-up!
Date: 6 Jan 1993 18:47:27 GMT
Organization: University of California, San Diego
NNTP-Posting-Host: weber.ucsd.edu

From charlie@umnstat.stat.umn.edu Wed Jan  6 08:18 PST 1993
From: "Charles Geyer" <charlie@umnstat.stat.umn.edu>
To: flim@weber.ucsd.edu
Subject: Re: ACE (Alternating Conditional Expectations)
Newsgroups: sci.math.stat
Organization: School of Statistics, University of Minnesota

In article <1ib1luINN84d@network.ucsd.edu> you write:
>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).



You said it yourself

   \theta(Y) - \phi(X)

is the residuals -- random error.

You might find the output simpler to understand if you use the option
to disallow nonlinear transformations of the response.  Then you get

   Y = phi(X) + error

and it looks much more like the usual regression situation.  You don't
then expect Y to be proportional to phi(X).  [Actually it won't do this
it insists on at least a standardizing linear transformation of Y, but
the points the same.]

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu

================================================

Thanks Charles, yes it all makes sense. ACE attempts to maximize
the correlation between \theta(Y) and \phi(X).
Thus if \hat{\theta}(Y) were proportional to \hat{\phi}(X),
the sample correlation would be 1. This would happen only in
extreme cases! Thanks again.
-- 
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
      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
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
From: ibe1109@draper.com (Ira Ekhaus)
Newsgroups: sci.math.stat
Subject: statistics of functions of a random process.
Date: 6 Jan 93 22:53:31 GMT
Organization: Draper Laboratory, Inc., Cambridge, MA

hello,

I'm looking into the statistics of functions of a process.
Specifically a low pass system response of an electro mechanical
system h(t+z),  where z is the random variable.

In the engineering text books I've been reviewing (mea culpa),
the standard correlation functions 
focus on the moments of a random process at deterministic offsets.
ie the mean
m(z) ==  <x(t)x'(t+z)>
x a random process, and z a delay parameter.

There is a lot of imposed structure on this system response function:
Limited bandwidth, conjugate symmetry, exponential decay,
that I'd like to exploit.

I'd appreciate  any references or applications where this 
type of analysis is done.

ira
Organization: TECHNION - Israel Institute of Technology.
Date: Wednesday, 6 Jan 1993 17:09:12 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: charlie@umnstat.stat.umn.edu (Charles Geyer)
Subject: Re: Nonrandom Random Numbers?
Organization: School of Statistics, University of Minnesota
Date: Tue, 12 Jan 1993 22:05:34 GMT

In article <dmurdoch.393.726873235@mast.queensu.ca> dmurdoch@mast.queensu.ca (Duncan Murdoch) writes:
>In article <1993Jan12.140114.620@hnrc.tufts.edu> jerry@hnrc.tufts.edu (Jerry Dallal) writes:
>>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.
>
>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.
>
>The generators tested were:
>
>CONG:  A linear congruential generator
>         X_n = (16807 X_{n-1}) mod (2^{31} - 1)
>
>R250:  A shift register algorithm:
>         X_n = X_{n-103} XOR X_{n-250}
>
>R1279: Another shift register algorithm:
>         X_n = X_{n-1063} XOR X_{n-1279}
>
>In R250 and R1279, "XOR" is a bitwise exclusive OR; it doesn't say how 
>many bits were used, but I'd assume 32 from the context.
>
>SWC:   A subtract with carry generator (Marsaglia et al, Comput Phys Commun
>60, p 345, 1990):
>
>         X_n = X_{n-22} - X_{n-43} - C
>         if X_n >= 0, C=0,
>         if X_n < 0, X_n = X_n + (2^32-5), C=1
>
>SWCW:   A combined subtract with carry Weyl generator (ibid):
>
>         Z_n = X_{n-22} - Z_{n-43} - C
>	 if Z_n >= 0, C=0
>         if Z_n < 0, Z_n = Z_n+(2^32-5), C=1
>	 Y_n = (Y_{n-1} - 362436069) mod 2^32
>	 X_n = (Z_n - Y_n) mod 2^32

Trouble is, none of these are "good" generators.  For safety, one should
XOR two "independent" generators as Marsaglia et al. do with the "super-duper"
and "ultra" generators.

Have any problems ever been reported with either of these?

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu
From: rons@hardy.u.washington.edu (Ronald Schoenberg)
Newsgroups: sci.math.stat
Subject: Re: SAS: White Heteroskedasticity-Consistent  Standard Errors Wanted
Date: 4 Jan 93 23:51:14 GMT
Article-I.D.: shelley.1iaihiINN7og
Organization: University of Washington, Seattle
NNTP-Posting-Host: hardy.u.washington.edu

In article <1993Jan4.112016.12341@mic.ucla.edu> iwelch@agsm.ucla.edu (Ivo Welch) writes:
>
>Well, let me correct my last statement(s).
>
>	Yes, SAS can compute the White (1980) heteroskedasticity-adjusted
>	covariance matrix. However, it can
>
>	neither	[a] output this matrix to a data set for further processing
>	nor	[b] use it to compute White se's or T-stats
>
>Essentially,  this procedure is  infeasible    for anything but   small   data
>problems.  If you  have 2,000  coefficients, be  prepared to  wade through   4
>million numbers, and compute the square root of 2,000 of them by hand.
>
>(Of course, I could  start learning IML programming,  and  compute  everything
>myself.  However, if I  have to  program, my  preference  is to do so in other
>languages [e.g. C, Fortran, Gauss,...] instead.)
>
>/ivo welch

My order of preference would be Gauss, C++, Fortran.  And you can order
Gauss with LINREG which computes Het.-Cons. standard errors for single
and multiple equation regression, and MAXLIK which will do the same for
user provided likelihood functions.


-- 
Ronald Schoenberg                    fax: 206-727-6521
University of Washington           email: rons@u.washington.edu
Newsgroups: sci.math.stat
From: idfriedm@hertz.uwaterloo.ca (id friedman)
Subject: history
Organization: University of Waterloo
Date: Sat, 9 Jan 1993 03:38:45 GMT

Anybody know any good mathematical "puzzles" type books about stats. ( O
containing many stat. problems).
From: german@ucbeh.san.uc.edu
Newsgroups: sci.math.stat
Subject: FTP sites for data
Date: 11 Jan 93 11:32:51 EST
Distribution: world
Organization: Univ. of Cincinnati




I'm teaching an introductory class in data analysis this quarter.  Most of the
students are graduate students in biology.  I'm looking for ftp sites for
datasets that I could use in class and for homework assignments.  Any
suggestions would be greatly appreciated.


--
R.Z. German
German@ucbeh.san.uc.edu




Newsgroups: sci.math.stat
From: charlie@umnstat.stat.umn.edu (Charles Geyer)
Subject: Re: Metropolis sampler
Keywords: Metropolis sampler
Organization: School of Statistics, University of Minnesota
Date: Sat, 9 Jan 1993 15:54:49 GMT

In article <1993Jan8.232157.1897@news2.cis.umn.edu> I wrote:

> The right question is how fast does the sampler mix.  This is discussed in
> the current issue of Statistical Science in papers by Gelman and Rubin and
> by myself with comments by about 10 discussants.

And I should have added the three discussion papers by Smith and Roberts, by
Besag and Green, and by Gilks, Clayton, Spiegelhalter, Best, McNeil, Sharples,
and Kirby in the next issue of JRSS B.  There are also about 20 discussants
there.

Lots of reading on Markov chain Monte Carlo.

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu
From: iwelch@agsm.ucla.edu (Ivo Welch)
Newsgroups: sci.math.stat
Subject: Re: SAS: White Heteroskedasticity-Consistent  Standard Errors Wanted
Date: 4 Jan 93 19:20:15 GMT
Organization: UCLA, Anderson Graduate School Of Management


Well, let me correct my last statement(s).

	Yes, SAS can compute the White (1980) heteroskedasticity-adjusted
	covariance matrix. However, it can

	neither	[a] output this matrix to a data set for further processing
	nor	[b] use it to compute White se's or T-stats

Essentially,  this procedure is  infeasible    for anything but   small   data
problems.  If you  have 2,000  coefficients, be  prepared to  wade through   4
million numbers, and compute the square root of 2,000 of them by hand.

(Of course, I could  start learning IML programming,  and  compute  everything
myself.  However, if I  have to  program, my  preference  is to do so in other
languages [e.g. C, Fortran, Gauss,...] instead.)

/ivo welch
Newsgroups: sci.math.stat,bit.listserv.stat-l
From: phatak@watserv1.uwaterloo.ca (Aloke Phatak)
Subject: Estimating bias
Summary: How to estimate bias in biased regression? 
Keywords: bias, biased regression 
Organization: University of Waterloo, Dept. of Chemical Engineering
Date: Thu, 7 Jan 1993 22:20:15 GMT


Just wondering - in using a biased estimator in the usual linear    
regression model given by

                         y = Xb + e

it's often possible to come up with an expression (as in principal
component regression, for example) for the bias of the estimator.
Unfortunately, it usually depends on b, the unknown parameter of
interest.  My question is this:  are there any methods to estimate
the bias given an analytical expression for it, even if that
expression depends on b?  I came across an article in the latest
Technometrics in which the authors show how bootstrapping can be
used to estimate bias, but I'm looking for simple methods that are
not computationally intensive.

Any suggestions or pointers to references would be appreciated.

Aloke Phatak

__
 +----------------------------------------------------------------------------+
 | Aloke Phatak                                                               |
 | Department of Chemical Engineering                                         |
 | University of Waterloo                        phatak@watserv1.uwaterloo.ca |
 | Waterloo, Ontario CANADA N2L 3G1                 (519) 885-1211, ext. 6982 | 
 +----------------------------------------------------------------------------+
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