My apologies. The email below accidentally only
went to Gregoire only. It turns out that I haven't quite reconnected to the list
correctly.. So...
------- Original Message -----
From: Michael Grant
To: Gregoire Dubois
Sent: Wednesday, August 09, 2006 8:48 AM
Subject: Re: AI-GEOSTATS: Log versus nscore transform Hi Gregoire,
Please forgive the rambling philosophical
response but I find your question interestingly provocative.
Is a preference of
lognormality mathematical elegance or is it tradition? I remember an era of
virtually automatic assumption of lognormality for two key classes of variables
in our business (nuclear/environmental): contaminant concentrations and
hydraulic conductivity. That practice lingers.
By the early and mid 1990's many
human and ecological risk assessors assumed lognormality of contaminant
concentrations in environmental media as an article of faith. 'The data are
skewed and hence lognormal.' In the US, I suspect that this state of affairs
reflected in part the issuance of a single document--the USEPA's approachable
supplemental guidance on calculating UCL for human health risk assessment (May
1992). While the EPA clearly evolved beyond that point, e.g., the agency's
work on bootstrapping UCLs, numerical/computational savvy of many but not
all 'street' assessors probably lagged.
This lag was due in part
to a mix of professional focus (toxicology versus
numbers), availability of tools, and convenience. Also the commercial
environmental business has significantly matured as a class of business and
we all know that it is crowded. Competitive pressures are significant,
and thorough data analysis--an expensive endeavor--is often a loser. The
convenience and economy of sanctioned lognormality (no-one reads the fine print)
beckons. For me going beyond nominal practice(?) almost always as been
on my time. However, that is the nature of things and as long as we
learn...:O) I think that the wider development, elucidation, and/or
implementation of computationally intensive techniques, e.g., bootstrap,
Monte Carlo, is changing at a fundamental level how we formulate our approaches
to many problems, vis-a-vis simulation. (Consider the transparency in the
formulation of resampling methods relative to the 'obscurity'
of traditional parametric statistics.)
Now regarding hydraulic
conductivity. Again lognormality is a long-standing tradition of nominal
practice. Certainly the last 25 years have witnessed a real evolution of
concepts and understanding with respect to hydraulic conductivity. And that
evolution certainly continues. But again, a mature, over-crowded environmental
business dictates nominal practice. Not everyone is a numbers-oriented
(hydro)geologist, and many who compile/interprets conductivity data have other
duties/interest. The convenience of long-standing tradition--all theory
aside--is powerful when faced with a need of a 'quick' characterization. BTW is
there a hydraulic conductivity analogy to the 92 EPA supplemental guidance
for concentrations UCLs? Sort of. I suspect that early co-kriging of water
levels (H) and K (T) has had a cementing impact on perception of K as
lognormal.
Is this pessimistic? Well, not
really. There are both academic and business opportunities here, and some
individuals will recognize those opportunities. 'Justification' is the sort
of issue that lead to progress both in the advance of theory and
the application of theory (technology). Also I do not mean for any of my
remarks to be judgemental or disparaging as to how others approach their work. I
am just trying to communicate what I perceive as (commercial and government
sector) participant in the environmental business for over 25 years.
In closing, some related scrap
thoughts: We operate (or should operate) in the context decision or
decisions being made and sometimes 'nominal' practice may suffice--although
that has to be reasonably demonstrated. I never have understood why decision
analysis has not had a better reception over the years. Also how are things
going to play out as some attempt to weave equifinality more into our
consciousness? Finally, all work has a finite shelf-life.
Best regards,
Mike
----- Original Message -----
|
Title: Log versus nscore transform
- AI-GEOSTATS: Re: Log versus nscore transform Isobel Clark
- RE: AI-GEOSTATS: Log versus nscore transform Gregoire Dubois
- Re: AI-GEOSTATS: Log versus nscore transform Peter Bossew
- Re: AI-GEOSTATS: Log versus nscore transform: K-... William V Harper
- Re: AI-GEOSTATS: Log versus nscore transform... Michael Grant
- RE: AI-GEOSTATS: Log versus nscore transform englund . evan
- AI-GEOSTATS: RE: Log versus nscore transform Isobel Clark
- AI-GEOSTATS: Sampling problem Peter Bossew
- Re: AI-GEOSTATS: Sampling problem Hellyer . Greg
- Re: AI-GEOSTATS: Sampling problem Edzer J. Pebesma
- Re: AI-GEOSTATS: Log versus nscore transform Michael Grant
- Re: AI-GEOSTATS: Log versus nscore transform Maribeth Milner
- Re: AI-GEOSTATS: Log versus nscore transform Michael Grant
- Re: AI-GEOSTATS: Log versus nscore transform Syed Shibli
- Re: AI-GEOSTATS: Log versus nscore trans... Michael Grant
- Re: AI-GEOSTATS: Log versus nscore ... Syed Shibli
- Re: AI-GEOSTATS: Log versus nscore transform Yetta Jager
- Re: AI-GEOSTATS: Log versus nscore transform Isobel Clark
- AI-GEOSTATS: Log versus nscore transform JW