Annals of Mathematical Sciences and Applications
Volume 3 (2018)
A unified Monte-Carlo jackknife for small area estimation after model selection
Pages: 405 – 438
We consider estimation of measure of uncertainty in small area estimation (SAE) when a procedure of model selection is involved prior to the estimation. A unified Monte-Carlo jackknife method, called McJack, is proposed for estimating the logarithm of the mean squared prediction error. We prove the second-order unbiasedness of McJack, and demonstrate the performance of McJack in assessing uncertainty in SAE after model selection through empirical investigations that include simulation studies and real-data analyses.
Computer intensive, jackknife, log-MSPE, measure of uncertainty, model selection, Monte-Carlo, second-order unbiasedness, small area estimation
The research of Jiming Jiang, Partha Lahiri, and Thuan Nguyen are partially supported by the NSF grants SES-1121794, SES-1534413 and SES-1118469, respectively. The research of Jiming Jiang and Thuan Nguyen are partially supported by the NIH grant R01-GM085205A1.
Received 16 February 2016