-
Propensity scores may be used for
matching or as
covariates,
alone or with
other matching variables or
covariates. 1.
Estimate propensity scores, e.g. with logistic...
- of the
covariates ("reverse" causation),
there are
omitted variables that
affect both the
dependent and
explanatory variables, or the
covariates are subject...
-
covariates include functional data.
Functional regression models can be
classified into four
types depending on
whether the
responses or
covariates are...
- of a
covariate consisting of
yearly values of
annual mean
atmospheric pressure at sea level. The
results showed that
inclusion of the
covariate allowed...
-
choosing covariates was
stepwise selection. That
approach only
improves prediction accuracy in
certain cases, such as when only a few
covariates have a...
-
reflects the
phenomenon that a
covariate is not
necessarily constant through the
whole study Time-varying
covariates are
included to
represent time-dependent...
- more
covariates that may be ****ociated with that
quantity of time. In a
proportional hazards model, the
unique effect of a unit
increase in a
covariate is...
-
models that was
developed to
handle high-dimensional
covariate vectors,
potentially with more
covariates than observations. The Theil–Sen
estimator is a simple...
- variable(s)
might describe treatment and the
continuous variable(s)
might be
covariates (CV)'s,
typically nuisance variables; or vice versa. Mathematically, ANCOVA...
- _{0}(\theta t)}
where θ {\displaystyle \theta }
denotes the
joint effect of
covariates,
typically θ = exp ( − [ β 1 X 1 + ⋯ + β p X p ] ) {\displaystyle \theta...
