Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Multivariable Bayesian curve-fitting under functional measurement error model

  • Hwang, Jinseub (Department of Computer Science and Statistics, Daegu University) ;
  • Kim, Dal Ho (Department of Statistics, Kyungpook National University)
  • Received : 2016.10.06
  • Accepted : 2016.11.08
  • Published : 2016.11.30
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Abstract

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

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Cited by

  1. Bayesian smoothing under structural measurement error model with multiple covariates vol.28, pp.3, 2017, https://doi.org/10.7465/jkdi.2017.28.3.709