- Isobel,

> NOT a law. There are distributions which do not

Is this the reason for transforming the data (only upto page 14).

> conform to this behaviour and (alas for us) the

> lognormal is one of them.

>

At the moment I am thinking kriging minimizes the variance of the

sampling distribution as I am also reading a book on classical

statistics.

Is this distribution common in elements other than gold and uranium.

>

John Sturgul was my lecturer in mine evaluation, I think he mentioned your

> The Central Limit theorem also does not apply to mixed

> distributions or in cases of non-stationarity. Mind

> you, neither does geostatistics................

>

1979 book in that course, but I did use it as a reference for a project I

did on geostatistics.

Thanks again,

Regards Digby Millikan B.Eng

Geolite Mining Systems

U4/16 First Ave.,

Payneham South SA 5070

Australia.

Ph: +61 8 84312974

digbym@...

http://www.users.on.net/digbym

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* Support to the list is provided at http://www.ai-geostats.org - I find this fascinating.

Apparently what I said is almost entirely wrong.

What I said was 'I was taught that.......' I do not

recollect Don Myers being in my classrooms as an

undergraduate (or during my MSC for that matter).

You know, I welcome criticism, especially when I get

things wrong. I have a big problem with people who do

not actually read what I write but react at some

visceral level to what they think I said.

Also, I must be really stupid, because the comments

given by Don include the statement

" If any of the conditions in the theorem are not

satisfied then the theorem may not apply. "

Which, I am fairly sure, is what I was trying to say.

Isobel Clark

http://uk.geocities.com/drisobelclark/resume

--- "Donald E. Myers" <myers@...> wrote:> Regrettably the following statement by I. Clark is

***********************************************************************

> almost entirely wrong

> See below for a correct statement of the CLT, the

> problem in part is

> simply carelessness in terminology and replacing

> correct

> statements/formulations by sort of heuristic ones

> (which are not correct)

> Donald E. Myers

> http://www.u.arizona.edu/~donaldm

>

> Isobel Clark wrote:

***************************************************************************

>

> >>The reason is simple and comprehensive....

> >>

> >>Assume a population with ANY distribution of

> >>elements. Then randomly select

> >>a number of sample elements from the population to

> >>characterize the

> >>underlying population. That distribution of sample

> >>elements ALWAYS tends

> >>toward a normal [Gaussian] distribution. And the

> >>mean and standard deviation

> >>of the sample distribution are unbiased

> >>representations of the mean and

> >>standard deviation of the underlying population.

> >>

> >

> >

>

>

__________________________________________________

> CLT

> Let X_1,...., X_n be a sequence of independent,

> identically distributed

> random variables with common mean m and common

> standard deviation

> sigma. Let Z_n be defined as a normalized sum

>

> Z_n = [S_n - m]/ (sigma/sqt root of n),

> S_n = [Z_1

> +.....+ X_n]/n

>

> S_n is the sample mean

>

> Let F_n(z) be the cumulative probability

> distribution function for Z_n

> and let G(z) be the cumulative probability

> distribution function for the

> standard Normal,. Then F_n(z) --> G(z) as n

> increases.

>

> Note two things about this statement, (1) the

> theorem does not say how

> "fast" the cdf for Z_n approaches the standard

> Normal, (2) the speed of

> convergence depends on z. Also the speed of

> convergence depends on the

> distribution type of the X_i's

>

> If any of the conditions in the theorem are not

> satisfied then the

> theorem may not apply. The convergence in this

> theorem is what is called

> "convergence in distribution", this is one of the

> weakest forms of

> convergence for a sequence of random variables.

> There are theorems that

> will give estimates or bounds on the speed of

> convergence. There are

> also special cases of this theorem that are somewhat

> simpler such as the

> the Normal approximation to the Binomial

>

> The simplest proof of the theorem above uses

> characteristic functions

> (Fourier Transforms of the densities).

>

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The original email,

> > >>The reason is simple and comprehensive....

was not written by Isobel, it came from W.D. Allen on sci.stat.math

> > >>

> > >>Assume a population with ANY distribution of

> > >>elements. Then randomly select

> > >>a number of sample elements from the population to

> > >>characterize the

> > >>underlying population. That distribution of sample

> > >>elements ALWAYS tends

> > >>toward a normal [Gaussian] distribution. And the

> > >>mean and standard deviation

> > >>of the sample distribution are unbiased

> > >>representations of the mean and

> > >>standard deviation of the underlying population.

which I posted in the summary of my replies.

Thankyou both for your help in this matter, I am currently reading

Practical Geostatistics 2000 and have ordered the statistics books

as recommended by Donald.

Regards Digby Millikan B.Eng

Geolite Mining Systems

U4/16 First Ave.,

Payneham South SA 5070

Australia.

Ph: +61 8 84312974

digbym@...

http://www.users.on.net/digbym

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* Support to the list is provided at http://www.ai-geostats.org - Isobel did not write the careless paragraph about the central limit theorem

(CLT) Don replied to, as pointed out by Digby. I wish to add something to

what Don said about the conditions under which the CLT applies, and that

people usually miss in considering the universality of the CLT. See below.

>> Let X_1,...., X_n be a sequence of independent,

Note also the sum operation. The CLT, more precisely called the Additive

>> identically distributed

>> random variables with common mean m and common

>> standard deviation

>> sigma. Let Z_n be defined as a normalized sum

>>

>> Z_n = [S_n - m]/ (sigma/sqt root of n),

>> S_n = [Z_1

>> +.....+ X_n]/n

>>

>> S_n is the sample mean

>>

>> Let F_n(z) be the cumulative probability

>> distribution function for Z_n

>> and let G(z) be the cumulative probability

>> distribution function for the

>> standard Normal,. Then F_n(z) --> G(z) as n

>> increases.

>>

>> Note two things about this statement, (1) the

>> theorem does not say how

>> "fast" the cdf for Z_n approaches the standard

>> Normal, (2) the speed of

>> convergence depends on z. Also the speed of

>> convergence depends on the

>> distribution type of the X_i's

CLT, applies to sums of pairwise independent random variables as n tends to

infinity. But if the operation is multiplication with equal-signed r.v.,

then convergence in distribution is towards the lognormal, not the normal.

It might well be that when considering natural phenomena, multiplicative

processes be more or equally common than additive ones, as we oftenly

observed skewed continuous data.

Rubén

http://webmail.udec.cl

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* Support to the list is provided at http://www.ai-geostats.org - Thanks to Rubén and Digby for pointing out what I had

misunderstood about Don Myers' email.

It had not occurred to me (duh) that the lines

starting '>' would be read as being from me rather

than part of a forwarded email.

Another score on the dumb side. Apologies for the

strong reaction to Don's email if (on this occasion)

he was not criticising my contribution.

Isobel

http://uk.geocities.com/drisobelclark

__________________________________________________

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