A Unified Monte-Carlo Jackknife for Small Area Estimation after Model Selection
Computation
2016-02-18 v1 Methodology
Abstract
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.
Cite
@article{arxiv.1602.05238,
title = {A Unified Monte-Carlo Jackknife for Small Area Estimation after Model Selection},
author = {Jiming Jiang and P. Lahiri and Thuan Nguyen},
journal= {arXiv preprint arXiv:1602.05238},
year = {2016}
}
Comments
33 pages, 3 figures