Please Note: This seminar will be given online.
|
Department seminar
Rakhi
Singh Link to join seminar: Hosted on Zoom |
Conquering the potentially crippling effect of too many interactions on the Gauss-Dantzig selector
Screening
designs
are
used
when,
with
limited
resources,
important
factors
are
to
be
identified
from
a
large
pool
of
factors.
Typically,
a
screening
experiment
will
be
followed
by
a
second
experiment
to
study
the
effect
of
the
identified
factors
in
more
detail.
As
a
result,
the
screening
experiment
should
ideally
screen
out
a
large
number
of
factors
to
make
the
follow-up
experiment
manageable,
without
screening
out
important
factors.
The
Gauss-Dantzig
Selector
(GDS)
is
often
the
preferred
analysis
method
for
screening
designs.
While
there
is
ample
empirical
evidence
that
fitting
a
main-effects
model
can
lead
to
incorrect
conclusions
about
the
factors
if
there
are
interactions,
including
two-factor
interactions
in
the
model
increases
the
number
of
model
terms
dramatically
and
challenges
the
GDS
analysis.
I
will
discuss
a
new
analysis
method,
called
Gauss-Dantzig
Selector
–
Aggregation
over
Random
Models
(GDS-ARM),
which
aggregates
the
effects
from
different
iterations
of
the
GDS
analysis
performed
using
different
sets
of
randomly
selected
interactions
columns
each
time.
The
GDS-ARM
draws
its
motivation
in
part
from
random
forests
by
building
many
models,
and
by
identifying
important
factors
after
running
a
GDS
analysis
on
all
of
these
models.
I
will
discuss
the
proposed
method,
study
its
performance,
and
elaborate
on
potential
future
directions.