Bhadra, Dhiman; Ghosh, Malay; Kim, Dalho Estimation of median household income for small areas: a Bayesian semiparametric approach. (English) Zbl 1278.62178 Calcutta Stat. Assoc. Bull. 64, No. 253-254, 115-142 (2012). From the summary: We put forward a semiparametric modeling procedure for estimating the median household income for all the U.S states. Our models include a nonparametric functional part for accommodating any unspecified time varying income pattern and also a state specific random effect to account for the within-state correlation of the income observations. Model fitting and parameter estimation is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo (MCMC) methodology. It is seen that the semiparametric model estimates can be superior to both the direct estimates and the Census Bureau estimates. Overall, our study indicates that proper modeling of the underlying longitudinal income profiles can improve the performance of model based estimates of household median income of small areas. MSC: 62P20 Applications of statistics to economics 62F15 Bayesian inference 60J22 Computational methods in Markov chains 62D05 Sampling theory, sample surveys 62G05 Nonparametric estimation Keywords:penalized spline; semiparametric modeling; MCMC PDFBibTeX XMLCite \textit{D. Bhadra} et al., Calcutta Stat. Assoc. Bull. 64, No. 253--254, 115--142 (2012; Zbl 1278.62178) Full Text: DOI