Incomplete covariates data in generalized linear models
Resource
Journal of Statistical Planning and Inference 79 (2):247-258
Journal
Journal of Statistical Planning and Inference
Pages
247-258
Date Issued
1999
Date
1999
Author(s)
Chen, Yi-Hau
Chen, Hung
Abstract
We consider regression analysis when part of covariates are incomplete in generalized linear models. The incomplete covariates could be due to measurement error or missing for some study subjects. We assume there exists a validation sample in which the data is complete and is a simple random subsample from the whole sample. Based on the idea of projection-solution method in Heyde (1997, Quasi-Likelihood and its Applications: A General Approach to Optimal Parameter Estimation. Springer, New York), a class of estimating functions is proposed to estimate the regression coefficients through the whole data. This method does not need to specify a correct parametric model for the incomplete covariates to yield a consistent estimate, and avoids the 'curse of dimensionality' encountered in the existing semiparametric method. Simulation results shows that the finite sample performance and efficiency property of the proposed estimates are satisfactory. Also this approach is computationally convenient hence can be applied to daily data analysis.
Type
journal article
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