- I have been using Variowin for my variogram analysis. My

covariograms are much better behaved compared to semivariograms, and

I would like to use the covariance to estimate the semivariance.

I understand that under the assumption of strict stationarity, which

is harder to satisfy than the intrinsic hypothesis, semivariance and

covariance are essentially equivalent. Since I have a non-stationary

process I would have expected that my covariograms to be just as bad

as my semivariograms, but this is not the case.

I've read that Variowin uses "non-ergotic"

covariance. Exactly what is this and how does it differ from regular

old covariance? I just want to make sure I understand the

assumptions of using non-ergotic covariance to estimate model

parameters.

Thanks!

Sara Kustron

Boston University Department of Geography

--

* To post a message to the list, send it to ai-geostats@...

* As a general service to the users, please remember to post a summary of any useful responses to your questions.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org - on 14/03/01 3:36, Sara Kustron at skustron@... wrote:

> I've read that Variowin uses "non-ergotic"

Non-ergodic covariances account for possibly changing means

> covariance. Exactly what is this and how does it differ from regular

> old covariance? I just want to make sure I understand the

> assumptions of using non-ergotic covariance to estimate model

> parameters.

across the region. Means used are different at h- and h+,

so the covariance formula is slightly different. Refer

Isaak/Srivastava's book Applied Geostatistics for the definition.

It's decidedly more "robust" to non-stationarity.

Syed

--

* To post a message to the list, send it to ai-geostats@...

* As a general service to the users, please remember to post a summary of any useful responses to your questions.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.org - Dear Sara:

Sorry, but the "non ergodic" variogram is an artifact!

If you do not believe to me, see the comments about my paper in NACOG 1996 or in Geostatistics, Volume 8, No.2.

The Mathematical proof is in my paper titled "Acerca del variograma no ergódico", in Spanish (difficult to get, if you wish I can

send a copy for you).

I think that is more easy to see an example (in my paper you have more examples):

Let a line with data (the data is very regular and has a trend) sampled at regular intervals of 1. Tha data are (20 data):

1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 9, 10, 10, 11, 12

Compute by hand or by your software the non ergodic and the classical variogram (the classical variogram is also non

ergodic!):

Conclusions:

The behavior of the non ergodic variogram, near the origin is linear, and, for the classical variogram is parabolic.

The non ergodic variogram has a range and a sill, and, the classical variogram always grows.

I attach a little Qbasic (or QuickBasic) program you can run.

Best regards,

Marco Alfaro.

Listing of the program:

n = 20

DIM z(n)

m = 0 ' mean

v = 0 ' variance

FOR i = 1 TO n

READ z(i)

m = m + z(i)

v = v + z(i) * z(i)

NEXT i

m = m / n ' mean

' the variance is = the non ergodic variogram in the origin

v = v / n - m * m

CLS : SCREEN 12

WINDOW (-10, -10)-(30, 80)

LINE (0, 0)-(20, 65), 8, B

LOCATE 3, 10: PRINT "In red, classical variogram, in green, non ergodic variogram"

FOR k = 0 TO n - 1

cov = 0 ' the non ergodic variogram.

head = 0

tail = 0

gama = 0 ' the classical variogram.

FOR i = 1 TO n - k

hh = z(i)

tt = z(i + k)

head = head + hh

tail = tail + tt

cov = cov + hh * tt

gama = gama + (hh - tt) * (hh - tt)

NEXT i

head = head / (n - k)

tail = tail / (n - k)

cov = cov / (n - k) - head * tail

gama = .5 * gama / (n - k)

CIRCLE (k, v - cov), .1, 2

PAINT (k, v - cov), 2

CIRCLE (k, gama), .1, 4

PAINT (k, gama), 4

NEXT k

a$ = INPUT$(1)

END

DATA 1,1,2,3,3,3,4,4,5,5,6,6,7,7,8,9,10,10,11,12

Sara Kustron wrote:

I have been using Variowin for my variogram analysis. My

covariograms are much better behaved compared to semivariograms, and

I would like to use the covariance to estimate the semivariance.

I understand that under the assumption of strict stationarity, which

is harder to satisfy than the intrinsic hypothesis, semivariance and

covariance are essentially equivalent. Since I have a non-stationary

process I would have expected that my covariograms to be just as bad

as my semivariograms, but this is not the case.

I've read that Variowin uses "non-ergotic"

covariance. Exactly what is this and how does it differ from regular

old covariance? I just want to make sure I understand the

assumptions of using non-ergotic covariance to estimate model

parameters.

Thanks!

Sara Kustron

Boston University Department of Geography

--

* To post a message to the list, send it to ai-geostats@...

* As a general service to the users, please remember to post a summary of any useful responses to your questions.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on

the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

* Support to the list is provided at http://www.ai-geostats.o

Sara Kustron wrote:

> I have been using Variowin for my variogram analysis. My

--

> covariograms are much better behaved compared to semivariograms, and

> I would like to use the covariance to estimate the semivariance.

>

> I understand that under the assumption of strict stationarity, which

> is harder to satisfy than the intrinsic hypothesis, semivariance and

> covariance are essentially equivalent. Since I have a non-stationary

> process I would have expected that my covariograms to be just as bad

> as my semivariograms, but this is not the case.

>

> I've read that Variowin uses "non-ergotic"

> covariance. Exactly what is this and how does it differ from regular

> old covariance? I just want to make sure I understand the

> assumptions of using non-ergotic covariance to estimate model

> parameters.

>

> Thanks!

> Sara Kustron

> Boston University Department of Geography

>

> --

> * To post a message to the list, send it to ai-geostats@...

> * As a general service to the users, please remember to post a summary of any useful responses to your questions.

> * To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

> * Support to the list is provided at http://www.ai-geostats.org

* To post a message to the list, send it to ai-geostats@...

* As a general service to the users, please remember to post a summary of any useful responses to your questions.

* To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list

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