HDTenEightyP/NetNews huggingface dataset:
From: hern@andy.bgsu.edu (Thomas Hern)
Newsgroups: sci.math.stat
Subject: Samuel Kotz to speak at NW Ohio ASA
Date: 28 Sep 92 19:05:54 GMT
Distribution: oh
Organization: Bowling Green State University B.G., Oh.


The NORTHWEST OHIO CHAPTER of the AMERICAN STATISTICAL ASSOCIATION
cordially invites you to a talk by SAMUEL KOTZ, Eugene Lukacs Visiting
Professor. Department of Mathematics and Statistics, Bowling Green
State University, and the University of Maryland, entitled

PROBABILITY and STATISTICS:  Uneasy Coexistence?
(Subtitle: Is STATISTICS a Science or Technology?)

on October 13, 1992, Tuesday, 7:30 PM
Room 459, Mathematical Sciences Building, Bowling Green State University,
Bowling Green Ohio.

Some Facts About the Speaker:  
Samuel Kotz is an extremely prolific writer and researcher.  He is
author and co-author of over 100 research papers in the fields of
Statistics, History of Statistics, Social Sciences and Quality
Control; and is author and editor of over 20 books, including the
10 volume Encyclopedia of Statistical Sciences.  In 1970 he was
elected Fellow of the American Statistical Association for his
contributions to Information Theory and Statistical Distribution
Theory; in 1974 he was elected Fellow of the Institute of Mathematical
Statisitics and became a member of the International Statistical
Institute; and in 1989 he was elected Fellow of the Royal Statistical
Society.  As of 1967 he has been listed in the Who's Who in Science,
and since 1971 has been listed in the Who's Who in America. Among
his numerous honors and awards, he was a recipient of the Distinguished
Scholar-Teacher award at the University of Maryland in 1984-85.

For more information, contact Prof. E. Pena (e-mail: pena@andy.bgsu.edu
or 372- 7461) of the Department of Mathematics and Statistics,
Bowling Green State University.


-- 
Thomas Hern
Department of Mathematics & Statistics	Internet:hern@andy.bgsu.edu
Bowling Green State Univ.		BITNET: hern@andy
Bowling Green OH 43403-0221		UUCP:..!osu-cis!bgsuvax!hern
From: whart@cs.ucsd.edu (Bill Hart)
Newsgroups: sci.math.stat
Subject: Generating Cauchy deviates
Date: 27 Sep 92 15:06:25 GMT

Can anyone point me to some code for generating samples with the Cauchy 
distribution?  I've browsed through statlib, but I didn't see anything
that had this distribution?

More generally, could people recommend (a) public domain packages which
have routines for generating various distributions [I've looked at ranlib,
but it doesn't have Cauchy deviates] or (b) a text which describes
how to do this sort of thing [I've looked in Numerical Recipies, but
it has a very limited selection of distributions].

I'll summarize my responses to the net.
Thanks in advance!

--Bill
From: ciar04@vaxa.strath.ac.uk
Newsgroups: sci.math.stat
Subject: Re: garch modelling software
Date: 29 Sep 92 10:01:15 GMT
Organization: Strathclyde University VAX Cluster

In article <1992Sep23.205017.25101@fnbc.com>, jja@fnbc.com (Joseph Alotta) writes:
> 
> Does anyone know a software package or a routine that can do GARCH  
> modelling?
> 
> Can someone also recommend some papers I might read?

Several econometric software in the market perform ARCH/GARCH modelling:

1)  TSP 4.2.  This has a command (ARCH) which performs standard
    GARCH(p,q) i.e. with conditional normal density of the error term.

2)  RATS.  Version 3.11 allows one to model univariate GARCH models.
    Version 4.0 (avaiable on PC only for the time being) allows multivariate
    GARCH modelling.  The advantage of RATS over TSP is that it allows
    you to do more "exotic" GARCH-type models, including EGARCH, AGARCH,
    GQARCH etc.  It allow allows you to specify different conditional
    densities.  Unfortunately, the version that I am currently using has
    some bugs in it (notably the log-gamma function), which prevents me
    from using say either a conditional t-distribution or a GED.

In addition, several people have developed FORTRAN programs for specific
GARCH type modelling.  Ken Kroner wrote programs for both univariate and
multivariate GARCH models, while Dan Nelson wrote an EGARCH program.

The literature for ARCH/GARCH is very extensive.  The more important papers
are:

1)  Bera, A.K., and Higgins, M.L. (1992), "A Survey of ARCH Models:
    Properties, Estimation and Testing.", University of Illinois at
    Urbana-Champaign working paper.

2)  Bollerslev, T. (1986), "Generalized Autoregressive Conditional
    Heteroskedasticity", Journal of Econometrics 31, 307-27.

3)  Bollerslev, T., Chou R.Y., Kroner, K.F. (1992), "ARCH Modeling in
    Finance:  A Review of the Theory and Empirical Evidence.", Journal
    of Econometrics 52, 5-59.

4)  Engle, R.F. (1982), "Autoregressive Conditional Heteroscedasticity
    with Estimates of the Variance of U.K. Inflation", Econometrica 50,
    987-1008.

5)  Higgins, M.L., and Bera, A.K. (1992), "A Class of Nonlinear ARCH
    Models.", International Economic Review (can't remember the volume
    and page number).

6)  Nelson, D.B. (1990), "Conditional Heteroskedasticity in Asset
    Returns: A New Approach", Econometrica 59, 347-70.

7)  Pagan, A.R., and Schwert, G.W. (1990), "Alternative Models for
    Conditional Stock Volatility.", Journal of Econometrics 45, 267-90.

Hope the above is useful.

Regards.

#----------------------------------#-------------------------------------------#
|  W.H.Watt                        #  JANET:                                   |
|  Dept. of Accounting & Finance   #           w.h.watt@uk.ac.strath.vaxa      |
|  University of Strathclyde       #  Rest of the World:                       |
|  Glasgow                         #           w.h.watt@vaxa.strath.ac.uk      |
|  United Kingdom                  #                                           |
#----------------------------------#-------------------------------------------#
From: rhh@alice.att.com (r hardin)
Newsgroups: sci.math.stat
Subject: Re: Ross Perot and Statistics
Date: 2 Oct 92 23:47:20 GMT
Article-I.D.: alice.23787
Organization: AT&T Bell Laboratories, Murray Hill NJ



>This, clearly, is one case where sample statistics are
>irrelevant.  We'll test the ENTIRE voting population in November.
>Lets hope that the correct techniques will be used THEN.

Actually, public opinion doesn't mean anything except in view of
a judgment that decides, eg. an election, which finally produces
a representation.

Otherwise it's a complete surface effect, produced by surveys and newspapers.

Just a point about the grammar. (ref. Derrida, The Other Heading, 1992)
Newsgroups: sci.math.stat
From: rizzo@cbnewsf.cb.att.com (anthony.r.rizzo)
Subject: Re: normal random number generato
Organization: AT&T
Date: Mon, 28 Sep 1992 18:35:35 GMT

In article <1992Sep28.155713.6316@dazixco.ingr.com> naftaly@dazixco.ingr.com writes:
>
>Thank to all the neters who send me information on the subject. It really helped.
>
>Naftaly
>

How about SHARING all that information with the rest of us?

I, for one, am quite interested in the subject too.

Tony Rizzo
From: ky04@ns1.cc.lehigh.edu (KUN YU)
Newsgroups: sci.math.stat
Subject: Variance of AxB?
Date: 1 Oct 92 21:12:44 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 $A {\times} B$?


-- 



                                                          _                  
ky04@lehigh.edu                                         / _ \
KYu, BioProcessing Institute                          /\ _|_ /\
111 Lehigh University                                    /|\
From: stvjc@gauss.med.harvard.edu (Vincent Carey 2-3265)
Newsgroups: sci.math.stat
Subject: package extensibility
Date: 2 Oct 92 16:11:50 GMT
Article-I.D.: hsdndev.1908
Distribution: usa
Organization: Harvard Medical School

development, deployments, tests and criticisms of new methodologies
are aided by statistical packages that are extensible.
the form of extensibility that i am concerned with
involves interfacing to compiled high-level language
subroutines and functions.  such extensions are well
supported in S and S-PLUS through .C() and .Fortran()
functions.  such extensions are supported in those versions
of SAS supporting SAS/TOOLKIT; apparently this is limited
at present to SAS running on VM systems.

there are many packages that i haven't put my hands on:
SPSS, SYSTAT, CSS-STATISTICA, BLSS, MINITAB, STATA.  do
these systems support this form of extension?  i would
appreciate pointers to the documents that describe protocols
for such extensions when supported.
Newsgroups: sci.math.stat
From: rmyers@ics.uci.edu (Richard E. Myers)
Subject: Re: Principle Components Analysis
Followup-To: sci.math.stat
Keywords: PCA
Reply-To: rmyers@ics.uci.edu (Richard E. Myers)
Organization: Univ. of Calif., Irvine, Info. & Computer Sci. Dept.
Date: Sun, 4 Oct 1992 06:22:55 GMT

> Steve Sullivan <sullivan@csn.org> writes:
> I'm interested in learning about principle components
> analysis, but none of the profs I've talked to have
> any references.
> 
> Can anyone recommend some good introductory books or
> articles?

I've become interested in using PCA to analyze a sequence of high
dimensional vectors (speech frames).  My SYSTAT manual and a look on
mlvl turned up the following references:


5. MCDONALD RP.
     COMMON PRINCIPAL COMPONENTS AND RELATED MULTIVARIATE METHODS - FLURY,B.
     Pub type:  Book Review.
     JOURNAL OF CLASSIFICATION, 1990, V7 N2:310-312.

6. Flury, Bernhard, 1951-
     Common principal components and related multivariate models / Bernhard
   Flury.  New York : Wiley, c1988.
     Series title:  Wiley series in probability and mathematical statistics.

8. Jackson, J. Edward.
     A user's guide to principal components / J. Edward Jackson.  New York :
   Wiley, c1991.
     Series title:  Wiley series in probability and mathematical statistics.
   Applied probability and statistics.

2. Harman, Harry Horace, 1913-1976.
     Modern factor analysis.  [Chicago] University of Chicago Press [1960].

1. Mulaik, Stanley A., 1935-
     The foundations of factor analysis [by] Stanley A. Mulaik.  New York,
   McGraw-Hill [1971, c1972].
     Series title:  McGraw-Hill series in psychology.

1. Gnanadesikan, Ram, 1932-
     Methods for statistical data analysis of multivariate observations / R.
   Gnanadesikan.  New York : Wiley, c1977.
     Series title:  Wiley series in probability and mathematical statistics.
     Series title:  A Wiley publication in applied statistics.

40. Mardia, K. V.
      Multivariate analysis / K. V. Mardia, J. T. Kent, J. M. Bibby.  London ;
    New York : Academic Press, 1979.
      Series title:  Probability and mathematical statistics.

Newsgroups: sci.math.stat
From: wwe@aifb.uni-karlsruhe.de (W. Weitz)
Subject: DOS program for solving equations wanted
Organization: AIFB, University of Karlsruhe (Baden / Germany)
Date: Mon, 28 Sep 1992 15:10:31 GMT


A friend of mine who is working on experimental design problems
is looking for a program to solve optimization problems with 
linear objective functions and quadratic polynomials as constraints.
Does anybody know about a MS-DOS or UNIX program for this purpose
(PD, if possible)?

Any help is greatly appreciated.

- W. Weitz <wwe@aifb.uni-karlsruhe.de>
From: whart@cs.ucsd.edu (Bill Hart)
Newsgroups: sci.math.stat
Subject: Cauchy deviates - summary of responses
Date: 28 Sep 92 03:00:07 GMT


Here's the summary of responses that I received concerning the
generation of Cauchy deviates:

Pretty much everyone who replied pointed out that the Cauchy distribution
can be generated by generating two standard normal deviates and taking
their quotient:  N(0,1)/N(0,1).  Barrett P. Eynon <barry@playfair.stanford.edu>
noted that the t distribution with 1 degree of freedom is equivalent to
the Cauchy distribution.  Finally, B. Narasimhan <naras@cda.mrs.umn.edu>
noted that 

	the ratio X/Y is Cauchy any time the pair (X,Y) is radially
	distributed. See Devroye, "Non Uniform Random Variate
	Generation", for more. Thus you can even use ratio of two
	coordinates of a point generated uniformly in the unit disk in
	2D.

Code which uses this last observation was provided by Lester Ingber
<ingber@umiacs.UMD.EDU> and is included at the end of this article.

Mark Plutowski <pluto@cs.ucsd.edu> noted that the Cauchy distribution
is also called the Lorentzian distribution, and is discussed as such in
Numerical Recipies.  Both NR and Ping Chou <choup@cgrb.orst.edu> noted
that you can generate a Cauchy deviate by taking a uniform deviate
on x \in [0,1] and computing tan(PI*x).  However, in NR they later
compute the Cauchy deviates using the same method used in Ingber's code,
so presumably this is more efficient.

Robert E George <regeorge@magnus.acs.ohio-state.edu> also noted that
there is a routine in IMSL for generating Cauchy deviates.

Thanks for everyone's replies!

--Bill


/** cauchy
 **
  #                      Prof. Lester Ingber                      #
  #                   ingber@alumni.caltech.edu                   #
 ** 
 **/

#define EPS     1.e-12

/* myrand() is any uniform number generator {0,1}. */

double cauchy(void)
{
        double x, y, z;
 
        for(;;) {
                x = TWO * myrand() - ONE;
                y = TWO * myrand() - ONE;
                z = x * x + y * y;
                if (z <= ONE)
                        break;
        }
        if (fabs(x) < EPS)
                x = (x < ZERO ? x - EPS : x + EPS);
 
        return (y / x);
}
From: beran@wizzle.Berkeley.EDU (ibidem)
Newsgroups: sci.math.stat
Subject: Re: robust location estimators
Keywords: robust estimation, biweight, trimmed mean, median, mean
Date: 1 Oct 92 03:58:54 GMT
Organization: Dept. of Statistics, U. C. Berkeley

> ... why robust location estimators have not become more popular.

One answer might be: too many candidates, with too many differing
theoretical optimality properties. As it stands, the problem of
robust estimation is not reasonably well-defined. It will take a
new approach, I suspect, at some future date.
From: tjfeue@bb1t.monsanto.com
Newsgroups: sci.math.stat
Subject: SAS users in Singapore?
Date: 1 Oct 92 16:38:48 CST
Organization: Monsanto Company, St. Louis, MO

Any major SAS users in Singapore?  I would appreciate any info, email
addresses, or conversation with you if you are one.

Ted Feuerbach - Scientific Systems Consultant
Newsgroups: sci.math.stat
From: lee@hecto.cs.umn.edu (YoungJun Lee)
Subject: approx. of binomial dist., help needed
Organization: University of Minnesota, Minneapolis, CSci dept.
Distribution: all
Date: Wed, 7 Oct 1992 02:58:57 GMT


 The stat. book I have just says that
  "Binomial distribution with n trials and success probability p can be
   approximated by possion distribution(when n is large, p is very small, and
   np is of moderate magnitude" and normal distribution(when n is large and
   p is not too near 0 or 1)"

 What's large n ? 
 What's "the very small p" or "not too near 0 or 1"?
 What's np of moderate magnitude ?

 For instance, n = 1000 and p = 0.001, then is 1000 is large?,
 is 0.001  is very small  or not too near 0 ?
 is np (1000 * 0.001 = 1) of moderate magnitude?
 Should I approximate this by possion dist or normal ?

 Would you please give me an idea when I should use possion/normal dist.
 to approximate binomial distribution.

 Thanks,


 YoungJun Lee
 lee@cs.umn.edu
From: maj@waikato.ac.nz
Newsgroups: sci.math.stat
Subject: Re: robust location estimators
Date: 27 Sep 92 17:37:14 +1200
Distribution: world
Organization: University of Waikato, Hamilton, New Zealand

In article <19s96oINNdd9@agate.berkeley.edu>, roberts@garnet.berkeley.edu (Seth Roberts) writes:
> I'm curious if anyone out there has made much use of robust
> location estimators, such as the trimmed mean and the biweight.
> And, if so, what happened?
>  
[deletion]
>  
> I'm curious why robust location estimators haven't become more popular.
[description of animal research deleted]
>  
> Seth Roberts
> Department of Psychology
> University of California at Berkeley

I believe that the reason robust location estimators
havn't taken off is that applied statisticians aren't
all that interested in location estimators per se - we
want to build models. The thrust of developments is
towards generalizing the linear model to non-linear,
non-normal, and non-iid situations. We'll do it
robustly if we know how, but if we don't we'll do it
anyway and patch things up with diagnostic statistics.
-- 
Murray A. Jorgensen [ maj@waikato.ac.nz ]    University of Waikato
Department of Mathematics and Statistics     Hamilton, New Zealand      
__________________________________________________________________
'Tis the song of the Jubjub! the proof is complete,
if only I've stated it thrice.'
From: chaas@coe.drexel.edu (Chuck Haas)
Newsgroups: sci.math.stat
Subject: test of two binomial proportions
Date: 29 Sep 92 14:29:41 GMT
Organization: Drexel University
X-Useragent: Nuntius v1.1b2

I have a two sample problem with quantal responses, e.g.

Group A				Group B
xa (dead)			xb (dead)
ya (live)				yb (live)

ta (total)      tb (total)

I wish to test the hypothesis that (xa/ta) = (xb/tb) against a one sided
alternative that (xa/ta) > (xb/tb).

Standard references give the Fisher's exact procedure for this, however
this is cumbersome if the numbers get large.  Also given in standard
references is the usual normal approximation to the binomial followed by
a two sample z - test.  However this latter case is not really good if
the proportion of responses is small (since the normal approximation to
the binomial fails in this case).

It seems to me, however, that an alternative approach would be a
likelihood ratio statistic to test whether the two groups were drawn from
a population with a single (versus two distinct) values of the population
proportion.  However I have not seen this described.  I wonder if anyone
has a reference to the approach I suggest, along with any comments
(power, robustness, etc.) versus either the Fisher exact test or the
normal approximation test.

I shall endeavor to summarize responses that are sent via e-mail to me.

Charles N. Haas
Professor, Environmental Engineering
Drexel University, Philadelphia PA 19104
======================================
CHaas@COE.Drexel.Edu
From: elias@fitz.TC.Cornell.EDU (Doug Elias)
Newsgroups: sci.math.stat
Subject: Inter-arrival data?
Date: 6 Oct 92 21:10:21 GMT
Distribution: sci
Organization: Tech. Integration Group

Hiya...

i'm trying to find some inter-arrival-time type data sets, either
single- or multi-variate, for use in a graduate termination project by
one of my students.  The intent is to compare/constrast the insights 
into the behavior and structure of the system made possible by a range
of different statistical and Systems Science methods.

Can anyone out there give me pointers towards data sets i could use
for such investigations?

Thanks,
doug
--
#  ____    |Internet:   elias@tc.cornell.edu
#dr _|_)oug|USmail:     Sci.Comp.Support/Cornell Theory Center
#  (_|     |            737 TheoryCtrBldg/C.U./Ithaca/N.Y./14853-3801
#  (_|__   |MaBelle:    607-254-8686   Fax: 607-254-8888
From: Decker@Themis.CS.UMASS.EDU (Keith Decker)
Newsgroups: sci.math.stat
Subject: Binomial Order Statistics?
Date: 1 Oct 92 14:29:49 GMT
Reply-To: decker@ariel.cs.umass.edu
Organization: Computer and Information Science, University of Massachusetts, Amherst, MA

Hi, I have (I hope) a simple question:

Say I have n samples Y1, Y2, ... Yn each from a Binomial
distribution with parameters p and n (that is, each Yi is the
sum of n Bernoulli trails with parameter p).

I would like to know the density function for the nth (max)
order statistic (i.e., the max of Y1,...Yn).

The answer may be in H.A. David's book "Order Statistics", but
it's out of our library and will take me a few weeks to have
recalled.

Thanks,
/Keith
decker@cs.umass.edu

Cooperative Distributed Problem Solving Lab
Department of Computer Science
University of Massachusetts at Amherst
From: hrubin@mentor.cc.purdue.edu (Herman Rubin)
Newsgroups: sci.math.stat
Subject: Re: Variance of AxB?
Date: 3 Oct 92 14:42:12 GMT
Organization: Purdue University Statistics Department

In article <23788@alice.att.com> rhh@alice.att.com (r hardin) writes:


>I seem to remember a handy trick for normal distributed numbers,
>the expected value of any product of them   <abcd> say  is

><abcd> = <ab> <cd> + <ac> <bd> + <ad> <bc>

>ie., just write every unique product of pairs

>which works for any order of product.

>From which, if I have remembered it correctly of course, you can
>compute any variance.

>Perhaps someone can verify the rule, which I am remembering from
>a distance of perhaps 20 years.

The rule is correct if the random variables have mean 0, and is
easily obtained by differentiating the moment generating function
the appropriate number of times and seeing which terms are not 0.

It is not valid if the means are not 0.

-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@snap.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!snap.stat!hrubin(UUCP)
From: sparapan@flash.ra.anl.gov (RA Sparapani RA/208/xxx gwing)
Newsgroups: sci.math.stat
Subject: Fourier Transform of Sinc Function
Date: 1 Oct 92 16:32:22 GMT
Organization: Argonne National Laboratory, Argonne IL


sci.math.stat:

I am having great difficulty computing the Fourier transform of the sinc
function, i.e. (sinx)/x.  If anyone knows of the solution or a reference
to a solution, I would be thankful.  The problem is that I need to 
integrate functions of the following forms.

sin(1+constant)x        cos(1+constant)x
----------------  and   ----------------
        x                       x

If anyone has information on the integrals of either of these two forms, I
would appreciate that, also.  Thanks.

Rodney
From: jsvrc@rc.rit.edu (J A Stephen Viggiano)
Newsgroups: sci.math.stat
Subject: Re: Let's Make a Deal!
Keywords: What are the odds?
Date: 28 Sep 92 14:59:14 GMT
Organization: RIT Research Corp

In article <1992Sep23.174950.518@uc.msc.edu> jwabik@uc.msc.edu (Jeff Wabik) writes:
>I understand that this "problem" was somewhat 'popular' recently.  Please
>accept my apologies if this subject has already been beaten to death.

[discription of "Monty Hall" scenario deleted]

"Beaten to death" is hardly the word for it. It was "beaten to death" ages
ago. A discussion of this problem may be had via anonymous ftp to
soda.berkeley.edu.