Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Frey and colleagues outline 10 trends showing scale of America's demographic transitions

Starr says surveys intended to predict recidivism assign higher risk to poor

Prescott and colleagues find incidence of noncompetes in U.S. labor force varies by job, state, worker education


ISR addition wins LEED Gold Certification

Call for Proposals: Small Grants for Research Using PSID Data. Due March 2, 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Next Brown Bag

Mon, March 9
Luigi Pistaferri, Consumption Inequality and Family Labor Supply

Bayesian penalized spline model-based inference for finite population proportion in unequal probability sampling

Publication Abstract

Chen, Q.X., Michael R. Elliott, and R.J. Little. 2010. "Bayesian penalized spline model-based inference for finite population proportion in unequal probability sampling." Survey Methodology, 36(1): 23-34.

We propose a Bayesian Penalized Spline Predictive (BPSP) estimator for a finite population proportion in an unequal probability sampling setting. This new method allows the probabilities of inclusion to be directly incorporated into the estimation of a population proportion, using a probit regression of the binary outcome on the penalized spline of the inclusion probabilities. The posterior predictive distribution of the population proportion is obtained using Gibbs sampling. The advantages of the BPSP estimator over the Hajek (HK), Generalized Regression (GR), and parametric model-based prediction estimators are demonstrated by simulation studies and a real example in tax auditing. Simulation studies show that the BPSP estimator is more efficient, and its 95% credible interval provides better confidence coverage with shorter average width than the HK and GR estimators, especially when the population proportion is close to zero or one or when the sample is small. Compared to linear model-based predictive estimators, the BPSP estimators are robust to model misspecification and influential observations in the sample.

Public Access Link

Browse | Search : All Pubs | Next