Use of auxiliary data in semi-parametric spatial regression with nonignorable missing responses

@inproceedings{Geraci2005UseOA,
  title={Use of auxiliary data in semi-parametric spatial regression with nonignorable missing responses},
  author={Marco Geraci and Matteo Bottai},
  year={2005}
}
We propose a method for reducing the error of the prediction of a quantity of interest when the outcome has missing values that are suspected to be nonignorable and the data are correlated in space. We develop a maximum likelihood approach for the parameter estimation of semi-parametric regressions in a mixed model framework. We apply the proposed method to phytoplankton data collected at fixed stations in the Chesapeake Bay, for which chlorophyll data coming from remote sensing are available… CONTINUE READING

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