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

PSC In The News

RSS Feed icon

Smock says cohabitation does not reduce odds of marriage

Smock cited in story on how low marriage rates may exacerbate marriage-status economic inequality

Frey says low turnover in House members related to lack of voter turnout among moderates

Highlights

Susan Murphy named Distinguished University Professor

Sarah Burgard and former PSC trainee Jennifer Ailshire win ASA award for paper

James Jackson to be appointed to NSF's National Science Board

ISR's program in Society, Population, and Environment (SPE) focuses on social change and social issues worldwide.

Next Brown Bag


PSC Brown Bags will return in the fall

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