|
Shapiro-Wilk, Shapiro-Francia, pp-plot regression
(similar to SW) are also all stock tools regarding normality tests.
At a deeper level, I can take
Gregoire's original comment:
"Moreover, one can frequently not be "sure" about the lognormality of the
analysed dataset"
to be 'one can not be sure that selection of a
lognormal MODEL is the most appropriate MODEL selection.' That is, part of
the rationale for choosing the lognormal transformation in a given situation may
be a belief that the data are lognormally distributed. I don't recall
anyone ever asserting that the given data 'are NSCORE distributed'.
:O)
My point is that one person may put more weight on
tying the transformation to something physical or some perceived to be physical,
whereas another practitioner may select the transformation perceived most
correct mathematically.
Regards,
Mike
----- Original Message -----
Sent: Thursday, August 10, 2006 12:08
PM
Subject: Re: AI-GEOSTATS: Log versus
nscore transform: K-S does not require binning
Peter,
While the chi-square does require binning, the
K-S (Kolmogorov-Smirnov) does not. While some do bin data for a K-S, one
of the reasons many use the K-S (or the supposedly slightly better
Anderson-Darling available in Minitab for example) is that it can be (&
should generally be) done w/o binning. Thus no binning decisions or
underling default of a given software package to influence the
outcome.
Best,
Bill
--
William V Harper, Mathematical Sciences, Otterbein College
Towers Hall 139, 1 Otterbein College
Westerville OH 43081-2006 USA
Office phone: 614-823-1417 Office Fax 614-823-3201
Faculty page: http://www.otterbein.edu/home/fac/WLLVHRPR
For the best in geostatistics: http://ecossenorthamerica.com/
Peter
Bossew wrote:
(original point by Gregoire:)
Moreover, one can frequently not be "sure" about the lognormality of
the analysed dataset, so why would one still take the risk of using
log-normal kriging?
(Anatoly:)
what means "not sure"??? Pearson and Kolmogorov-Smirnov tests will be
used :-))
Anatoly,
Chi2 and KS-tests also require binning the data, and different binning
gives different p values, in general !
related questions:
1) the Weibull distribution can sometimes be used to describe datasets
with heavy tails.
(e.g., www.itl.nist.gov/div898/handbook/eda/section3/eda3668.htm )
However I don't know how to transform Weibull -> normal and back. Does
somebody have experience ?
2) What is the effect of deviations of data from normal distr., in
particular due to the presence of extrema (outliers, hot/cold spots), to
structural analysis and to estimation, be it of kriging or simulation type
?
regards, Peter
-----------------------------------------------------
Peter Bossew
European Commission (EC)
Joint Research Centre (JRC)
Institute for Environment and Sustainability (IES)
TP 441, Via Fermi 1
21020 Ispra (VA)
ITALY
Tel. +39 0332 78 9109
Fax. +39 0332 78 5466
Email: [EMAIL PROTECTED]
WWW: http://rem.jrc.cec.eu.int
"The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission."
+
+ To post a message to the list, send it to [email protected]
+ To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/
+ + To post a message to the list, send it to
[email protected] + To unsubscribe, send email to majordomo@ jrc.it with no
subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND
Subscribe/Unsubscribe requests to the list + As a general service to list
users, please remember to post a summary of any useful responses to your
questions. + Support to the forum can be found at
http://www.ai-geostats.org/
|
