Generalized Linear Density Ratio Models

Fits a generalized linear density ratio model (GLDRM).
A GLDRM is a semiparametric generalized linear model.
In contrast to a GLM, which assumes a particular exponential family distribution,
the GLDRM uses a semiparametric likelihood to estimate the reference distribution.
The reference distribution may be any discrete, continuous, or mixed exponential
family distribution. The model parameters, which include both the regression
coefficients and the cdf of the unspecified reference distribution, are estimated
by maximizing a semiparametric likelihood. Regression coefficients are estimated
with no loss of efficiency, i.e. the asymptotic variance is the same as if the
true exponential family distribution were known.

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### Description file content

- Package
- gldrm
- Type
- Package
- Title
- Generalized Linear Density Ratio Models
- Version
- 1.4
- Description
- Fits a generalized linear density ratio model (GLDRM).
A GLDRM is a semiparametric generalized linear model.
In contrast to a GLM, which assumes a particular exponential family distribution,
the GLDRM uses a semiparametric likelihood to estimate the reference distribution.
The reference distribution may be any discrete, continuous, or mixed exponential
family distribution. The model parameters, which include both the regression
coefficients and the cdf of the unspecified reference distribution, are estimated
by maximizing a semiparametric likelihood. Regression coefficients are estimated
with no loss of efficiency, i.e. the asymptotic variance is the same as if the
true exponential family distribution were known.
- Depends
- R (>= 3.2.2)
- Imports
- stats (>= 3.2.2)
- Suggests
- testthat (>= 1.0.2)
- License
- MIT + file LICENSE
- LazyData
- TRUE
- RoxygenNote
- 6.0.1
- NeedsCompilation
- no
- Packaged
- 2017-12-06 00:46:23 UTC; mike
- Author
- Michael Wurm [aut, cre],
Paul Rathouz [aut]
- Maintainer
- Michael Wurm
- Repository
- CRAN
- Date/Publication
- 2017-12-06 09:58:46 UTC