Estimation of Finite Population Domain Means: Then and Now

• Activity Number: 490
• Type: Invited
• Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Jiming Jiang*+ and Thuan Nguyen
• Companies: University of California at Davis and Oregon Health & Science University
• Keywords: Asymptotics ; Mean Square Prediction Error ; Model Misspecification ; Nested-Error Regression ; Robustness ; Small Area Estimation

Abstract

The first part of the talk is based on a paper published in JASA(2006; joint with P. Lahiri), in which we introduce a general methodology for producing a model-assisted empirical best predictor (EBP) of a finite population domain mean using data from a complex survey. Our method improves on the commonly used design-consistent survey estimator by using a suitable mixed model. Unlike a purely model-based EBP, the proposed model-assisted EBP converges in probability to the customary design-consistent estimator as the domain and sample sizes increase. The convergence is shown to hold with respect to the sampling design, irrespective of the assumed mixed model, a property commonly known as design-consistency. The second part of the talk introduces a new approach to small area estimation. We derive the best predictive estimator (BPE) of the fixed parameters under the nested-error regression model. This leads to a new prediction procedure, called observed best prediction (OBP). We show that the OBP is more reasonable than the empirical best linear unbiased predictor (EBLUP) and can significantly outperform the latter when the underlying model is misspecified.