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Home
HCV project*
Breast cancer
Biliary Cirrhosis
economics
pharmaco-ics*
environment
transplants*
pediatrics
MicroArray QC
Diabetes
Impact$$
| | Welcome to the home page of SLSTM
technology created by Data Scientist James Minor
See Impact$$ for sample of documented success of SLSTM
projects.
"Better clinical prognostics mean effective healthcare at lower cost!",
SLSguy.
"Quality is the survival probability of a product or service (process) in a
competitive market", SLSguy, 1994
"Quality supports business, but statistics drives quality!", SLSguy.
*completed sites. Big news in Diabetes site. Publications noted in HCV clinical and microarray
QC
SLSTM
was originally called SMILES in 1970's
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In a data matrix consider each row (profile or pattern) as a
physical object.
Each column becomes a property of the objects. Two rows (objects) are similar if
their property values are "near" to each other. Statistical inference
based on object similarity is the original SLSTM concept. The concept
was first publicly referenced in support of environmental research:
Miller, C., Filkin, D., Owens, A., Steed J., and
Jesson, P. (1981), “Two-D Model of Stratospheric Chemistry and Transport”,
Journal of Geophysical Research, 86 (C12), 12039-12065
Design of Experiment (DOE)
A statistical model is trained on data. DOE assures the training data
produces a stable model. DOE specifies a minimal set of critical locations in
the functional domain for a stable model. Only a stable model is suitable for validation.
A DOE
reference for non-statisticians: Box, Hunter, and Hunter and/or the Echip
system of Bob Wheeler.
Support Points, a consequence of the SLSTM
concept
Support points are a set of key locations in the function's
domain that enable an SLSTM model to adequately represent functionality given good data coverage. "Super" support points (super vectors)
can do this task with minimal locations and minimal data coverage. This reduces model complexity
and enhances all forms of prediction including extrapolation and sparse
interpolation.
Validation, model performance in real time
What is the true error rate of the model?
The model must be stable. Model instability compromises realistic validation.
What is the best error rate possible given the set of data variables?
The SLSTM system appears to approach this limit.
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