Smoothing spline in multivariable semiparametric regression through fully bayesian approach

Article type: Research Article

Authors: Diana, Rita

Affiliations: The West Sumatera Province Central Bureau of Statistics, Indonesia | E-mail: rita.diana@bps.go.id

Abstract: A multivariable semiparametric regression model is a combination of parametric and nonparametric regressions, the parametric component of which refers to polynomial patterns, while its nonparametric component does not have certain pattern. This nonparametric component can be fitted with smoothing spline function. This reearch is aimed to to develop a multivariable semiparametric regression model through fully Bayesian approach for cross-sectional data. The development is meant to be implemented in analysing Open Unemployment Rate (OUR) in East Java Province, Indonesia. The result applying the model in estimating the Open Unemployment Rate (OUR) in East Java province reveals that multivariable semiparametric regression model with parametric component, based on macro economy, corresponds to linear and the nonparametric components corresponds to cubic smoothing spline function using fully Bayesian approach. The parametric component includes the percentage of population with higher education and regional minimum wage. The nonparametric component includes economic growth, population density, large-sized and medium-sized industries ratio. In conclusion, the smoothing spline modeling using fully Bayesian approach shows better performance than using Bayesian approach.

Keywords: Fully Bayesian, Gibbs sampling, MCMC, multivariable semiparametric regression, smoothing spline, open unemployment rate

DOI: 10.3233/MAS-180424

Journal: Model Assisted Statistics and Applications, vol. 13, no. 2, pp. 123-132, 2018