Efficient mean estimation in log-normal linear models

Haipeng Shen, University of North Carolina at Chapel Hill
Zhengyuan Zhu, Iowa State University

This is a manuscript of an article published as Shen, Haipeng, and Zhengyuan Zhu. "Efficient mean estimation in log-normal linear models." Journal of Statistical Planning and Inference 138, no. 3 (2008): 552-567. DOI: 10.1016/j.jspi.2006.10.016. Posted with permission.


Log-normal linear models are widely used in applications, and many times it is of interest to predict the response variable or to estimate the mean of the response variable at the original scale for a new set of covariate values. In this paper we consider the problem of efficient estimation of the conditional mean of the response variable at the original scale for log-normal linear models. Several existing estimators are reviewed first, including the maximum likelihood (ML) estimator, the restricted ML (REML) estimator, the uniformly minimum variance unbiased (UMVU) estimator, and a bias-corrected REML estimator. We then propose two estimators that minimize the asymptotic mean squared error and the asymptotic bias, respectively. A parametric bootstrap procedure is also described to obtain confidence intervals for the proposed estimators. Both the new estimators and the bootstrap procedure are very easy to implement. Comparisons of the estimators using simulation studies suggest that our estimators perform better than the existing ones, and the bootstrap procedure yields confidence intervals with good coverage properties. A real application of estimating the mean sediment discharge is used to illustrate the methodology.