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On Classical Measurement Error within a Bayesian Nonparametric Framework

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Recent Developments in Statistics and Data Science (SPE 2021)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 398))

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Abstract

This paper studies the impact of classical measurement error on a Dependent Dirichlet Process (DDP). Specifically, we study a Simulation-Extrapolation (SIMEX) algorithm, adapted to a nonparametric Bayesian framework, that assesses the impact of measurement error by inducing even further error in the covariate. We illustrate the algorithm via a battery of numerical experiments.

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Author information

Authors and Affiliations

  1. School of Mathematics, University of Edinburgh, Edinburgh, Scotland

    Emmanuel Bernieri & Miguel de Carvalho

Authors
  1. Emmanuel Bernieri
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  2. Miguel de Carvalho
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Corresponding author

Correspondence to Miguel de Carvalho .

Editor information

Editors and Affiliations

  1. NOVA School of Science and Technology, Caparica, Portugal

    Regina Bispo

  2. University of Évora, Évora, Portugal

    Lígia Henriques-Rodrigues

  3. University of Évora, Évora, Portugal

    Russell Alpizar-Jara

  4. University of Edinburgh, Edinburgh, UK

    Miguel de Carvalho

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Bernieri, E., de Carvalho, M. (2022). On Classical Measurement Error within a Bayesian Nonparametric Framework. In: Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., de Carvalho, M. (eds) Recent Developments in Statistics and Data Science. SPE 2021. Springer Proceedings in Mathematics & Statistics, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-031-12766-3_24

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