hajek.constrained.symmetric: Computational workhorse function. Maximizes the value of...
In jrzubizarreta/scbounds: Shape-constrained partial identification of a population mean under unknown probabilities of sample selection
Description
Usage
Arguments
Value
Description
Computational workhorse function. Maximizes the value of hatmu among all Hajek
ratio estimators that satisfy the following properties:
- The ratio of the sampling weights gamma is bounded
- The resulting CDF is symmetric to within tolerance delta.
Usage
1
hajek.constrained.symmetric(Xcdf, xvals, sampling.ratio, center, delta)
Arguments
Xcdf
The raw, unweighted, empirical CDF.
xvals
The points at which Xcdf is specified.
sampling.ratio
The bound on the sampling ratio.
center
The center of symmetry.
delta
The tolerance parameter.
Value
Xcdf.weighted A weighted version of Xcdf that maximizes hatmu
jrzubizarreta/scbounds documentation built on May 20, 2019, 5:26 p.m.
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Description Usage Arguments Value
Description
Computational workhorse function. Maximizes the value of hatmu among all Hajek ratio estimators that satisfy the following properties: - The ratio of the sampling weights gamma is bounded - The resulting CDF is symmetric to within tolerance delta.
Usage
1 | hajek.constrained.symmetric(Xcdf, xvals, sampling.ratio, center, delta)
|
Arguments
Xcdf |
The raw, unweighted, empirical CDF. |
xvals |
The points at which Xcdf is specified. |
sampling.ratio |
The bound on the sampling ratio. |
center |
The center of symmetry. |
delta |
The tolerance parameter. |
Value
Xcdf.weighted A weighted version of Xcdf that maximizes hatmu
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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