Confidence intervals
Here we describe two methods for forming confidence intervals for empirical Bayes estimands (Empirikos.EBayesTarget
).
F-Localization based intervals
Empirikos.FLocalizationInterval
— TypeFLocalizationInterval(flocalization::Empirikos.FLocalization,
convexclass::Empirikos.ConvexPriorClass,
solver,
n_bisection = 100)
Method for computing frequentist confidence intervals for empirical Bayes estimands. Here flocalization
is a Empirikos.FLocalization
, convexclass
is a Empirikos.ConvexPriorClass
, solver
is a JuMP.jl compatible solver.
n_bisection
is relevant only for combinations of target
, flocalization
and convexclass
for which the Charnes-Cooper transformation is not applicable/implemented. Instead, a quasi-convex optimization problem is solved by bisection and increasing n_bisection
increases accuracy (at the cost of more computation).
AMARI intervals
Empirikos.AMARI
— TypeAMARI(convexclass::Empirikos.ConvexPriorClass,
flocalization::Empirikos.FLocalization,
solver,
plugin_G = KolmogorovSmirnovMinimumDistance(convexclass, solver))
Affine Minimax Anderson-Rubin intervals for empirical Bayes estimands. Here flocalization
is a pilot Empirikos.FLocalization
, convexclass
is a Empirikos.ConvexPriorClass
, solver
is a JuMP.jl compatible solver. plugin_G
is a Empirikos.EBayesMethod
used as an initial estimate of the marginal distribution of the i.i.d. samples $Z$.
Interface
StatsAPI.confint
— MethodStatsBase.confint(method::AMARI,
target::Empirikos.EBayesTarget,
Zs;
level=0.95)
Form a confidence interval for the Empirikos.EBayesTarget
target
with coverage level
based on the samples Zs
using the AMARI
method
.