Thursday, October 1, 2020 4:00 pm
-
4:00 pm
EDT (GMT -04:00)
Please Note: This seminar will be given online.
|
Statistics and Biostatistics Seminar Series
Ashley
Petersen Link to join seminar: Hosted on Webex. |
Data-Adaptive Regression Modeling in High Dimensions
In
recent
years,
it
has
become
easier
and
less
expensive
to
collect
and
store
large
amounts
of
data
in
a
number
of
fields.
This
has
amplified
interest
in
the
development
of
statistical
methods
to
adequately
model
this
data.
With
high-dimensional
data,
the
traditional
plots
used
in
exploratory
data
analysis
can
be
limiting,
given
the
large
number
of
possible
predictors.
Thus,
it
can
be
helpful
to
fit
sparse
regression
models,
in
which
variable
selection
is
adaptively
performed,
to
explore
the
relationships
between
a
large
set
of
predictors
and
an
outcome.
For
maximal
utility,
the
functional
forms
of
the
covariate
fits
should
be
flexible
enough
to
adequately
reflect
the
unknown
relationships
and
interpretable
enough
to
be
useful
as
a
visualization
technique.
In
this
talk,
we
will
provide
an
overview
of
recent
work
in
the
area
of
sparse
additive
modeling
that
can
be
used
for
visualization
of
relationships
in
big
data.
In
addition,
we
will
present
recent
novel
work
that
fuses
together
the
aims
of
these
previous
proposals
in
order
to
not
only
adaptively
perform
variable
selection
and
flexibly
fit
included
covariates,
but
also
adaptively
control
the
complexity
of
the
covariate
fits
for
increased
interpretability.