glmMisrep - Generalized Linear Models Adjusting for Misrepresentation
Fit Generalized Linear Models to continuous and count
outcomes, as well as estimate the prevalence of
misrepresentation of an important binary predictor.
Misrepresentation typically arises when there is an incentive
for the binary factor to be misclassified in one direction
(e.g., in insurance settings where policy holders may purposely
deny a risk status in order to lower the insurance premium).
This is accomplished by treating a subset of the response
variable as resulting from a mixture distribution. Model
parameters are estimated via the Expectation Maximization
algorithm and standard errors of the estimates are obtained
from closed forms of the Observed Fisher Information. For an
introduction to the models and the misrepresentation framework,
see Xia et. al., (2023)
<https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.