Conformal normal curvature and detection of masked observations in multivariate null intercept measurement error models

Abstract

Measurement errors occur very commonly in practice. After fitting the model, influence diagnostics is an important step in statistical data analysis. The most frequently used diagnostic method for measurement error models is the local influence. However, this methodology may fail to detect masked influential observations. To overcome this limitation, we propose the use of the conformal normal curvature with the forward search algorithm. The results are presented through easy to interpret plots considering different perturbation schemes. The proposed methodology is illustrated with three real data sets and one simulated data set, two of which have been previously analyzed in the literature. The third data set deals with the stability of the hygroscopic solid dosage in pharmaceutical processes to ensure the maintenance of product safety quality. In this application, the analytical mass balance is subject to measurement errors, which require attention in the modeling process and diagnostic analysis.

Keywords:

• Local influence diagnostics
• structural relationship
• forward search
• error in variables model
• latent variable
• likelihood displacement

Acknowledgments

The authors would like to thank the Associate Editor and three referees for their constructive comments and recommendations that definitely helped improve the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.