COVRATIO statistic for replicated linear functional relationship model

Abstract

Abstract Replicated linear functional relationship model (LFRM) can be categorized under the errors-in-variables model where variables involved are measured with error. However, the presence of outliers in dataset significantly impacts the parameter estimation. We extend the use of the COVRATIO statistic which has been successfully used in unreplicated LFRM for detecting the outliers. A simulation study is used to obtain the cut-off point at 10% upper percentiles. An illustration of this procedure is presented for its potential in a real data set. The procedure successfully identifies the outlier present in the data set.