Bayesian measurement error correction in structured additive distributional regression with an application to the analysis of sensor data on soil–plant variability

@article{Pollice2019BayesianME,
  title={Bayesian measurement error correction in structured additive distributional regression with an application to the analysis of sensor data on soil–plant variability},
  author={Alessio Pollice and Giovanna Jona Lasinio and Roberta Rossi and Mariana Amato and Thomas Kneib and Stefan Lang},
  journal={Stochastic Environmental Research and Risk Assessment},
  year={2019},
  volume={33},
  pages={747-763}
}
The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models. We consider a class of continuous, discrete and mixed univariate response distributions with potentially all parameters depending on a structured additive predictor. Markov chain Monte Carlo enables a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of unobserved error-free covariate values… 
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