Score tests in a generalized linear model with surrogate covariates

Bayesian Model Averaging for Generalized Linear Models with Missing Covariates

We address the problem of estimating generalized linear models (GLMs) when the outcome of interest is always observed, the values of some covariates are missing for some observations, but imputations are available to fill-in the missing values. Under certain conditions on the missing-data mechanism and the imputation model, this situation generates a trade-off between bias and precision in the ...

Model averaging estimation of generalized linear models with imputed covariates

We address the problem of estimating generalized linear models when some covariate values aremissing but imputations are available to fill-in the missing values. This situation generates a bias-precision tradeoff in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a p...

Score Tests in Generalized Linear Measurement Error Models

Score tests for zero-inflation in generalized linear models

The authors develop score tests of goodness-of-fit for discrete generalized linear models against zero-inflation. The binomial and Poisson models are treated as examples and in the latter case, the proposed test reduces to that of Broek (1995). Some simulation results and an illustrative example are presented.

Two-step Estimation for a Generalized Linear Mixed Model with Auxiliary Covariates

Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose a two-step inference procedure for GLMM with missing covariate data. It is shown that the proposed estimator is consistent and asymptotically normal with covariance matrix that can be easily estima...