Abstract
As
sensor
and
computer
technology
continues
to
improve,
it
becomes
a
normal
occurrence
that
we
confront
with
high
dimensional
data
sets.
As
in
many
areas
of
industrial
statistics,
this
brings
forth
various
challenges
in
statistical
process
control
and
monitoring.
This
new
high
dimensional
data
often
exhibit
not
only
cross-‐correlation
among
the
quality
characteristics
of
interest
but
also
serial
dependence
as
a
consequence
of
high
sampling
frequency
and
system
dynamics.
In
practice,
the
most
common
method
of
monitoring
multivariate
data
is
through
what
is
called
the
Hotelling’s
T2
statistic.
For
high
dimensional
data
with
excessive
amount
of
cross
correlation,
practitioners
are
often
recommended
to
use
latent
structures
methods
such
as
Principal
Component
Analysis
to
summarize
the
data
in
only
a
few
linear
combinations
of
the
original
variables
that
capture
most
of
the
variation
in
the
data.
In
this
paper,
we
discuss
the
effect
of
autocorrelation
(when
it
is
ignored)
on
multivariate
control
charts
based
on
these
methods
and
provide
some
practical
suggestions
and
remedies
to
overcome
this
problem.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 28th Quality and Productivity Research Conference |
| Publication date | 2011 |
| Publication status | Published - 2011 |
| Event | 28th Quality and Productivity Research Conference - Virginia Tech, Roanoke, United States Duration: 8 Jun 2011 → 10 Jun 2011 Conference number: 28 |
Conference
| Conference | 28th Quality and Productivity Research Conference |
|---|---|
| Number | 28 |
| Location | Virginia Tech |
| Country/Territory | United States |
| City | Roanoke |
| Period | 08/06/2011 → 10/06/2011 |
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