Statistical and Multivariate R&D
Over the last 3+ decades of performing statistical and multivariate
analysis, we have gained a great deal of experience in this area.
Some of the approaches used here and not readily available elsewhere
include the following:
· HVA (Hyperplanar Vector Analysis - unique to GXStat): A system to
decompose a multivariate data set into end-members and proportions,
regardless of whether or not the end-members are contained in the
data or not, where the original data may not be defined by constant
row sum (Polytopic Vector Analysis - PVA - requires this). The
composition of the end-members are presented in the original metric
and there are tools used to define the proper number of end-members.
· FCM (Fuzzy c-Means): A program based on the article by Bezdek,
Ehrlich and Full, Computers & Geosciences 10(2-3):191-203 (1983). An
alternative to hard clustering that minimized the effect of extreme
data - a common component of large, geologic data sets.
· FNV (Fuzzy N-Varieties): A program based on the book by Bezdek,
Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer
Academic Publishers Norwell, MA (1981). This program addresses the
issues with clustering about cluster center points and allows for
clusters to be defined by lines, line segments, planes, hyperplanes
and so forth in addition to cluster centers. This program is useful
the decomposition of big data into more meaningful sub-spaces.
Given GXStat's broad experience not only creating and developing
several of these programs, performing similar research and
development to solve the customer's problem is a service we offer.
GXStat's researchers have performed this service for both commercial
and Department of Defense entities.
For more detailed information on GXStat's Statistical and
Multivariate R&D services,
contact us.