Approximate bounded influence estimation for longitudinal data with outliers and measurement errors |
| |
Authors: | Lang Wu Jin Qiu |
| |
Institution: | a Department of Statistics, University of British Columbia, 333-6356 Agricultural Road, Vancouver, B.C., Canada V6T 1Z2 b School of Mathematics and Statistics, Zhejiang University of Finance and Economics, Hangzhou 310018, China |
| |
Abstract: | Mixed effects models or random effects models are popular for the analysis of longitudinal data. In practice, longitudinal data are often complex since there may be outliers in both the response and the covariates and there may be measurement errors. The likelihood method is a common approach for these problems but it can be computationally very intensive and sometimes may even be computationally infeasible. In this article, we consider approximate robust methods for nonlinear mixed effects models to simultaneously address outliers and measurement errors. The approximate methods are computationally very efficient. We show the consistency and asymptotic normality of the approximate estimates. The methods can also be extended to missing data problems. An example is used to illustrate the methods and a simulation is conducted to evaluate the methods. |
| |
Keywords: | Measurement error Missing data Mixed models Outliers Robust methods |
本文献已被 ScienceDirect 等数据库收录! |
|