detection of outliers and influential observations in linear mixed measurement error models with liu estimation

In this paper, the case deletion approach and mean shift outlier model are developed to identify influential and outlier observations using the liu corrected likelihood estimator in linear mixed measurement error models when multicollinearity is present. we derive a corrected score test statistic for outlier detection based on mean shift outlier models. furthermore, according to the liu corrected likelihood estimator, several cook's distance is constructed for influence diagnostics. a parametric bootstrap procedure is used to obtain empirical distribution of the test statistic and a simulation study is conducted to demonstrate the performance of the diagnostic criteria. finally, a real example is provided to illustrate the performance of the test statistics.