HDTenEightyP/NetNews huggingface dataset:
From: mld900@anusf.anu.edu.au (Murray Dow)
Newsgroups: sci.math.stat,sci.math.num-analysis
Subject: Re: Help
Date: 12 Oct 1992 23:51:03 GMT
Organization: ANU Supercomputer Facility
Distribution: world
Reply-To: mld900@anusf.anu.edu.au
NNTP-Posting-Host: 150.203.5.3


In article <1992Oct11.062034.12719@oucsace.cs.ohiou.edu>, ravi@bobcat.ent.ohiou.edu (Pattalachinti) writes:
|> Can anybody out there have some very  rubost and 
|> My belief
|> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|> GRAVITY EXISTED BEFORE NEWTON DISCOVERED IT       !!!!!!!!!!!!!  
|> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Sorry but Isaac did not discover gravity. People of his time were aware that
objects were attracted to earth. What Isaac realised was that gravity
extended to the Moon, and worked out its orbit (he had to invent calculus).
--
Murray Dow                         GPO Box 4 Canberra ACT 2601 Australia
Supercomputer Facility             Phone: +61 6 2495028
Australian National University     Fax:   +61 6 2473425
mld900@anusf.anu.edu.au
From: hrubin@pop.stat.purdue.edu (Herman Rubin)
Newsgroups: sci.math.stat
Subject: Re: approx. of binomial dist., help needed
Date: 8 Oct 92 16:02:49 GMT
Distribution: all
Organization: Purdue University Statistics Department

In article <1992Oct7.025857.28031@news2.cis.umn.edu> lee@hecto.cs.umn.edu (YoungJun Lee) writes:

> 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.

There are some simple theorems about the accuracy, but these are conservative
for some purposes.  However, they are good guidelines.

The error in the Poisson approximation to the binomial is bounded by np^2,
where this is the sum of the absolute errors to the probabilities.  This can
be moderately improved for large variance.  In your example above, this would
make the error bound .001, and the actual error is about .000552.

The error in the central limit theorem has two main terms; a multiple of
the standardized third moment divided by the standard deviation, and a
bounded expression divided by the variance.  These expansions and error
bounds can be found in the standard works.  Since the variance is .999,
this approximation will be quite poor.

There are formulas to compute the binomial cdf by continued fractions 
from the probability of the endpoint.
-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@pop.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!pop.stat!hrubin(UUCP)
Newsgroups: sci.math.stat,misc.invest
From: "Gary A. Woodward" <p00140@psilink.com>
Subject: Re: Use of Statistical Techniques in Stock Selection
Organization: Performance Systems Int'l
Date: Wed, 14 Oct 1992 12:31:59 GMT
X-Mailer: PSILink (3.2)

There is a magazine called the Technical Analysis of Stocks and Commodities.
They can be reached at 1.800.832.4642 or 206.938.0570.
Newsgroups: sci.math.stat
From: freytag@seas.gwu.edu (Richard Freytag)
Subject: Variance estimation Q.?
Followup-To: freytag@seas.gwu.edu
Summary: Parameter estimation in non-I.I.D. Gaussian white noise
Organization: George Washington University
Date: Thu, 15 Oct 1992 20:30:42 GMT

I am posting this for a collegue.  Please email me with your responses
and I will pass them along.  

Question follows:

I have a parameter estimation problem which sounds simple but 


The following question is being posted for a collegue.  Email me 
your responses and I will pass them along...

I have a parameter estimation problem which sounds simple but 
seems to have been missed in the literature.  We have a Gaussian 
random variable X with mean and variance given by the unknown 
parameters mu_x, and sigma_x.  We get observations Yi = X + Ni.
The Ni are independent, non-identical Gaussian random variables 
with varying but known variances.  Our observation Yi consists of 
the observation Yi and the variance of the particular noise at 
time i, what I call sigma_i.  I have derived the MLE equations 
for mu_x, and sigma_x and find that they are non-linear and not 
solvable in closed form.  While they can be solved for numerically 
we are working in real time and cannot afford a Newton-Raphson type 
numerical solution.  I have also worked the problem from a linear 
least squares approach, and while this works OK for estimating mu_x, 
they do not work for estimating sigma_x as sigma_x is an inherently 
non-linear function of the observations.  Can anyone direct me to 
any literature regarding this problem.

Thanks in advance for your help,
Mark Levin
Newsgroups: sci.math.stat
From: carlson@cwis.unomaha.edu (Brian D. Carlson)
Subject: Petersberg Paradox
Summary: Why is it called Petersberg Paradox
Keywords: Petersberg Paradox Statistics
Organization: University of Nebraska at Omaha
Date: Mon, 12 Oct 1992 15:13:36 GMT

	Hello,

	I am looking for information concerning the nameing of a certain
paradox. It is called the Petersberg Paradox. Can anyone tell me why it
is called the Petersberg Paradox?

	The Petersberg Paradox goes something like this:

	The probability distribution of X is given by f(x) = (1/2)^x for
x = 1, 2, 3, ..., infinity. The expectation E(2^x) does not exist.

	in other words a players expectation is infinite if he/she is to
receive 2^x dollars when in a series of flips of a balanced coin, the
first head appears on the xth toss.

	If available sources would be appreciated.

Thanks,
Brian D. Carlson
carlson@unomaha.edu
From: BOYDJ@QUCDN.QueensU.CA (Jeff Boyd)
Newsgroups: sci.math.stat
Subject: Re: Fitting normal distribution with skew.
Date: 11 Oct 92 17:44:55 GMT
Article-I.D.: QUCDN.92285.134455BOYDJ
Organization: Queen's University at Kingston


What mathematical limits apply to luminosity?  Is it bounded on one or
both ends of its range?  If so, I'd look into (linear functions of) the
gamma and beta families, respectively.  They can be fit using the method
of moments or by iterative MLE.

You can also look for simple transformations that eliminate the skew or
introduce upper and/or lower bounds.


jeff
From: wsnell@ops.agsci.colostate.edu (Warren Snelling)
Newsgroups: sci.math.stat
Subject: Bivariate normal density/distribution functions
Date: 15 Oct 92 15:06:45 GMT
Reply-To: wsnell@ops.agsci.colostate.edu (Warren Snelling)
Organization: Colorado State University, College of Agriculture

Several papers I am studying refer to "standard bivariate
normal density and distribution functions" with no further  
explaination; these computations are necessary for the methodology
presented.  None of the readily available statistics references 
have provided insight, and I did not see anything obvious in 
Statlib.  I would appreciate any reference to algorithms for
computing these functions.

Thanks in advance

warren
wsnell@ops.agsci.colostate.edu
Newsgroups: sci.math.stat
From: stephen@mont.cs.missouri.edu (Stephen Montgomery-Smith)
Subject: Re: approx. of binomial dist., help needed
Organization: University of Missouri
Date: 12 Oct 92 17:40:11 GMT

Littlewood wrote a fairly comprehensive paper about finding approximations
to the tail of the binomial distribution.  I don't recall its exact
reference, but I could probably find out if anyone is interested.

Stephen

Newsgroups: sci.math.stat
From: radha@riemann.gsfc.nasa.gov (Poovendran Raadhakrishnan)
Subject: Noise
Organization: NASA Goddard Space Flight Center, Greenbelt MD
Date: Thu, 15 Oct 1992 17:46:52 GMT


I have encountered the following problem:

	The system I am working with has been mechanically disturbed.
	I am told that the disturbance can be treated as Gaussian in 
	2-Dimension. The effect of this perturbation is further 
	assumed to be low-pass filtering of the "true" image by the 
	Gaussian disturbance noise. Is it valid to assume that the 
	lowpass filter is Gaussian ? . If not, what is the right way
	to approach this problem? 
	Thanks,
Newsgroups: sci.math.stat
Subject: Interface '93
From: rm32085@mcvax2.d48.lilly.com (JOHNS DON JR)
Date: 8 Oct 1992 10:43 EST  
Distribution: world,local
Organization: Lilly Research Laboratories
Keywords: the Interface between statistics and computer science
News-Software: VAX/VMS VNEWS 1.41    Lines: 7

Where and when is Interface '93?
Thanks.  

Don Johns                   \  djohns@lilly.com
Eli Lilly and Company        \
Indianapolis, Indiana 46285   \
317-276-3398                   \
Newsgroups: sci.math.stat
From: ciar04@vaxa.strath.ac.uk
Subject: Re: book recommendation
Organization: Strathclyde University VAX Cluster
Date: Wed, 14 Oct 1992 20:44:30 GMT

In article <81531@ut-emx.uucp>, txwizard@ccwf.cc.utexas.edu (txwizard) writes:
> hi!  i would greatly appreciate your recommendation on an introductory 
> time series and forecasting book.  i had some introduction to the 
> above mentioned subjects in econometrics but need more to pursue
> my interest in financial analysis.
> 
> i thank you in advance for the info.
> 
> mike kim
> txwiard@ccwf.cc.utexas.edu

S. Taylor's Modelling Financial Time Series is an excellent text on
looking at financial time series.  Though the book is a bit dated (1986),
it contains your basic ARMA type models, plus the ARCH and GARCH (called
ARMACH by Taylor) models.

#----------------------------------#-------------------------------------------#
|  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                  #                                           |
#----------------------------------#-------------------------------------------#

Newsgroups: sci.math.stat
Newsgroups: sci.math.stat

At the University of Concepcion, Chile, they are using a HP720 workstation for
their "heavy duty" number crunching.  They are interested in obtaining any 
shareware that does statistics or numerical analysis.  Does anyone have any
suggestions.  (They do have access to the network and mail.)

Please send replies to me, and I will forward them.


Kathy Hall
Hewlett - Packard, Integrated Circuits Business Div. - Corvallis
hpcvnb.cv.hp.com
503-750-4074
Newsgroups: sci.math.stat
Subject: Re: Help on Boxplotts in S needed!!!!!!!
From: oconnor@tahi.isor.vuw.ac.nz (Paul Oconnor)
Date: Wed, 7 Oct 1992 19:26:40 GMT
Organization: Institute of Statistics and Operations Research, VUW, Wellington, NZ


In article <1992Oct6.225803.59485@cc.usu.edu>, sljbr@cc.usu.edu writes:
|> I have a problem with boxplots on the S.
|> The problem is:
|> I have 5 boxplots to be made. I want all the five to have a common x and y
|> axes. How is this possible??
|> Could anyone help me out??
|> Thanx a lot in advance.


lets say the data is in a five column matrix called data. Then call 

boxplot(split(t(data), i), ylim = c(min(data), max(data)), 
        names = c("name1","2","3","4","5"),
        ylab = "y label", xlab = "x label")

If the matrix has the column names then you can let the names
option equal; names = dimnames(data)[2]. This will pop the names in for you.

What do you mean by a common x axis?

Cheers

Paul O'Connor                          Phone: +64 4 472-1000
Institute of Statistics and              FAX: +64 4 495-5118
Operations Research,                   Email: oconnor@isor.vuw.ac.nz
Victoria University of Wellington      Snail: PO Box 600,Wellington,New Zealand
-- 
Paul O'Connor                          Phone: +64 4 472-1000
Institute of Statistics and              FAX: +64 4 495-5118
Operations Research,                   Email: oconnor@isor.vuw.ac.nz
Victoria University of Wellington      Snail: PO Box 600,Wellington,New Zealand
Newsgroups: sci.math.stat
From: ayman@eecs.umich.edu (Ayman I. Kayssi)
Subject: Re: Rational functions: a linear model?
Organization: ACAL, EECS, University of Michigan, Ann Arbor
Date: Sun, 11 Oct 1992 19:30:15 GMT

hrubin@pop.stat.purdue.edu (Herman Rubin) wrote:
>In article <1992Oct11.153538.12821@zip.eecs.umich.edu> ayman@eecs.umich.edu (Ayman I. Kayssi) writes:
>>y = a0 + a1*x + ... + am*x^m + b1*(-y)*x + b2*(-y)*x^2 + ... + bn*(-y)*x^n
>
>If you are trying a regression on your transformed equation, the estimates
>will not even be consistent because of dependent variables on the right side.


TableCurve (from Jandel) lists the following form under *linear* equations:
    
    a + c x + e x^2 + ...
y = ---------------------
    1 + b x + d x^2 + ...

How do they (non-iteratively) calculate the parameters?
--
Ayman I. Kayssi                               |    ayman@eecs.umich.edu
EECS Department                               +------------------------
University of Michigan                        |          (313) 764-8033
Newsgroups: sci.math.stat
From: ayman@eecs.umich.edu (Ayman I. Kayssi)
Subject: Rational functions: a linear model?
Organization: ACAL, EECS, University of Michigan, Ann Arbor
Date: Sun, 11 Oct 1992 15:35:18 GMT

I'm trying to fit data to a rational function. What is the transformation
that I need to make to have a linear model?
y = (a0 + a1*x + a2*x^2 + ... + am*x^m)/(1 + b1*x + b2*x^2 + ... + bn*x^n)
means that:
y + b1*y*x + b2*y*x^2 + ... + bn*y*x^n = a0 + a1*x + ... + am*x^m
or:
y = a0 + a1*x + ... + am*x^m + b1*(-y)*x + b2*(-y)*x^2 + ... + bn*(-y)*x^n

I tried this and I'm not getting the right answers...is this the right 
way to do it?

--
Ayman I. Kayssi                               |    ayman@eecs.umich.edu
EECS Department                               +------------------------
University of Michigan                        |          (313) 764-8033
From: chaas@coe.drexel.edu (Chuck Haas)
Newsgroups: sci.math.stat
Subject: Goodness of Fit with Zero Degrees of Freedom
Date: 8 Oct 92 14:49:19 GMT
Organization: Drexel University
X-Xxmessage-Id: <A6F9C2718C0118D7@n1-24-215-coe-abbotts.coe.drexel.edu>
X-Xxdate: Thu, 8 Oct 92 02:48:17 GMT
X-Useragent: Nuntius v1.1.1d12


Suppose I have a set of categorical data.
						trait present				trait absent				group  totals
group	a	a1												a0											at
group b		b1												b0											bt
... etc.

The group totals should be regarded as fixed.  I fit a model to this
data.  If the model has k parameters and there are m groups, then I know
that I could do a goodness of fit test (e.g., Fisher chi-square or
likelihood ratio) and compare to the chi-square distribution with m-k
degrees of freedom.

Suppose, however, that I fit a model with k=m parameters.  How do I then
test goodness of fit.  In other words, how much slippage should be
allowed before I reject the null hypothesis of model adequacy
(conditional on the parameters)?

Charles N. Haas
Professor, Environmental Engineering
Drexel University, Philadelphia PA 19104
======================================
CHaas@COE.Drexel.Edu
Newsgroups: sci.math.stat
From: rawson@umuc.umd.edu
Subject: NCSS Software Package
Organization: The University of Maryland University College
Date: Fri, 16 Oct 1992 16:57:18 GMT

We are reviewing the NUMBER CRUNCHER STATISTICAL SYSTEM (student version)
produced by Dr. Jerry L. Hintze of Kaysville, Utah.  While some of us like
the easy-to-use menu-driven interface, we are concerned that we have not
seen much literature reviewing the package.

We would be grateful for any information about the package from other
reviewers, end-users, etc.

Thanks in advance for your help.

Jim Rawson
The Office of Instructional Development
The University of Maryland University College
College Park, MD

rawson@umuc.umd.edu
From: fiyk303@utxvms.cc.utexas.edu
Subject: test
Organization: University of Texas @ Austin
Date: 13 Oct 92 08:32:06 CST

In article <carlson.718902816@cwis>, carlson@cwis.unomaha.edu (Brian D. Carlson) writes:
> 	Hello,
> 
> 	I am looking for information concerning the nameing of a certain
> paradox. It is called the Petersberg Paradox. Can anyone tell me why it
> is called the Petersberg Paradox?
> 
> 	The Petersberg Paradox goes something like this:
> 
> 	The probability distribution of X is given by f(x) = (1/2)^x for
> x = 1, 2, 3, ..., infinity. The expectation E(2^x) does not exist.
> 
> 	in other words a players expectation is infinite if he/she is to
> receive 2^x dollars when in a series of flips of a balanced coin, the
> first head appears on the xth toss.
> 
> 	If available sources would be appreciated.
> 
> Thanks,
> Brian D. Carlson
> carlson@unomaha.edu
>
From: sljbr@cc.usu.edu
Newsgroups: sci.math.stat
Subject: Help on Boxplotts in S needed!!!!!!!
Date: 6 Oct 92 22:58:01 MDT
Organization: Utah State University

I have a problem with boxplots on the S.
The problem is:
I have 5 boxplots to be made. I want all the five to have a common x and y
axes. How is this possible??
Could anyone help me out??
Thanx a lot in advance.
-balaji rajagopalan,
utah water research laboratory.
utah state univ.,
logan, utah
ut - 84321-8200.
sljbr@cc.usu.edu or
rbala@kernel.uwrl.usu.edu

Newsgroups: sci.math.stat,sci.math.num-analysis
From: txwizard@ccwf.cc.utexas.edu (txwizard)
Newsgroups: sci.math.stat
Subject: book recommendation
Date: 12 Oct 92 16:28:10 GMT
Reply-To: txwizard@ccwf.cc.utexas.edu (txwizard)
Organization: The University of Texas at Austin, Austin TX
Originator: txwizard@bashful.cc.utexas.edu

hi!  i would greatly appreciate your recommendation on an introductory 
time series and forecasting book.  i had some introduction to the 
above mentioned subjects in econometrics but need more to pursue
my interest in financial analysis.

i thank you in advance for the info.

mike kim
txwiard@ccwf.cc.utexas.edu

Newsgroups: sci.math.stat
From: jsvrc@rc.rit.edu (J A Stephen Viggiano)
Subject: Re: approx. of binomial dist., help needed
Organization: RIT Research Corp
Date: Thu, 8 Oct 1992 20:23:10 GMT

In article <1992Oct7.025857.28031@news2.cis.umn.edu> lee@hecto.cs.umn.edu (YoungJun Lee) writes:
>
> 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)"

For the Poisson approximation, it is much more important for p to be small
than it is for n to be large. The approximation is quite acceptable for n = 1
with p <= 0.01. This can be seen by looking at the relative error in the
variance; it involves only p.

With n = 30, a p of 0.05 or less is usually considered satisfactory.

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

For the gaussian approximation, we were taught that the tails of the
approximating distribution should be well within the limits of 0 and n. Thus,
np - 5 (npq)**0.5 should be greater than zero, and np + 5(npq)**0.5 should
be less than n, where q = 1 - p. In other words, the mean should be at least
five standard deviations from both zero and n. Again, this is a rule of
thumb. For p = 0.5 (the best case), n should be at least 25. Smaller or
larger values of p will require a larger n to reduce the coefficient of
variation.
From: packer@amarna.gsfc.nasa.gov (Charles Packer)
Newsgroups: sci.math.stat
Subject: Re: approx. of binomial dist., help needed
Date: 8 Oct 92 10:55:00 GMT
Distribution: all
Organization: Dept. of Independence
News-Software: VAX/VMS VNEWS 1.4-b1

In article <1992Oct7.025857.28031@news2.cis.umn.edu>, lee@hecto.cs.umn.edu (YoungJun Lee) writes...
> What's np of moderate magnitude ?


How about: use the Poisson when np is "much less" than the
square root of n? This is from a chapter I photocopied out of a
book years ago, for which I don't have title/author, sorry. The
reference doesn't define what "much less" is, though. 
Newsgroups: sci.math.stat
From: hrubin@pop.stat.purdue.edu (Herman Rubin)
Subject: Re: Rational functions: a linear model?
Organization: Purdue University Statistics Department
Date: Sun, 11 Oct 1992 18:29:58 GMT

In article <1992Oct11.153538.12821@zip.eecs.umich.edu> ayman@eecs.umich.edu (Ayman I. Kayssi) writes:
>I'm trying to fit data to a rational function. What is the transformation
>that I need to make to have a linear model?
>y = (a0 + a1*x + a2*x^2 + ... + am*x^m)/(1 + b1*x + b2*x^2 + ... + bn*x^n)
>means that:
>y + b1*y*x + b2*y*x^2 + ... + bn*y*x^n = a0 + a1*x + ... + am*x^m
>or:
>y = a0 + a1*x + ... + am*x^m + b1*(-y)*x + b2*(-y)*x^2 + ... + bn*(-y)*x^n

>I tried this and I'm not getting the right answers...is this the right 
>way to do it?

If you are trying a regression on your transformed equation, the estimates
will not even be consistent because of dependent variables on the right side.
There are ways that this can be handled (an INDICATION of how to do it is
to use higher powers of x to identify the situation, and to estimate it
as a single equation in a complete system), but this is very definitely
not optimal.  There is NO way to transform a non-linear model to a linear
model preserving the assumptions.  Figure out your assumptions, and then
use the appropriate statistical procedures.

A word of warning is appropriate.  Fitting a polynomial or a rational
function is very ill-conditioned, no matter how it is done.  You may
need not only the services of a good mathematical statistician, but
also an excellent numerical analyst.  You may very well need the numerical
analyst even if there are no random errors; crude programs are likely to
break down.

-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@pop.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!pop.stat!hrubin(UUCP)
From: freeman@olympus.as.arizona.edu (Jonathan Freeman)
Newsgroups: sci.math.stat
Subject: Fitting normal distribution with skew.
Date: 8 Oct 92 21:53:19 GMT
Distribution: usa
Organization: University of Arizona, Tucson, AZ

I am unclear on how to fit a normal model with a skew component to a
histogram. I can, of course, compute the skew of the data set, 
but that does not tell me if it is a better model than one
without that factor. I would also like to use robust regression
because the data set appears to have a few outliers. References?
For those interested, the data is a histogram of the lumimosities
of low mass stars. Email or post.
From: henrik@stat.tamu.edu (Henrik Schmiediche)
Newsgroups: sci.math.stat
Subject: Search for Multivariate Time Series Analysis Software
Date: 14 Oct 92 16:00:01 GMT
Article-I.D.: tamsun.1992Oct14.160001.24517
Organization: Department of Statistics, TAMU


      Hi, 

I am trying to survey the availability of software to perform
multivariate time series analysis. By multivariate I mean 3 or more
data sets. If anyone knows of software packages, public domain, free or
comercial, could you send me a quick email with the name of the
software package? If you happen to know the scope of the analysis that
the package performs (ie, spectral estimation, AR spectral estimation,
window estimators, etc.) I would appreciate that info too.

Any response will be appreciated. If you know of a bibliography and/or
references concerning this matter, that info would be helpful too.

Thanks,

      - Henrik

--
Henrik Schmiediche, Dept. of Statistics, Texas A&M, College Station, TX 77843
E-mail: henrik@stat.tamu.edu  |  Tel: (409) 845-9447   |  Fax: (409) 845-3144
Newsgroups: sci.math.stat,misc.invest