Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Bayesian curve-fitting with radial basis functions under functional measurement error model

  • Hwang, Jinseub (National Evidence-based Healthcare Collaborating Agency) ;
  • Kim, Dal Ho (Department of Statistics, Kyungpook National University)
  • Received : 2015.03.23
  • Accepted : 2015.04.27
  • Published : 2015.05.31
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Abstract

This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.

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References

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Cited by

  1. A comparative study in Bayesian semiparametric approach to small area estimation vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1433
  2. Multivariable Bayesian curve-fitting under functional measurement error model vol.27, pp.6, 2016, https://doi.org/10.7465/jkdi.2016.27.6.1645