Weight Smoothing for Generalized Linear Models Using a Laplace Prior.

ABSTRACT: When analyzing data sampled with unequal inclusion probabilities, correlations between the probability of selection and the sampled data can induce bias if the inclusion probabilities are ignored in the analysis. Weights equal to the inverse of the probability of inclusion are commonly used to correct possible bias. When weights are uncorrelated with the descriptive or model estimators of interest, highly disproportional sample designs resulting in large weights can introduce unnecessary variability, leading to an overall larger mean square error compared to unweighted methods. We describe an approach we term 'weight smoothing' that models the interactions between the weights and the estimators as random effects, reducing the root mean square error (RMSE) by shrinking interactions toward z

SUBMITTER: Xia X 

PROVIDER: S-EPMC5719898 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature