Conformal normal curvature and detection of masked observations in multivariate null intercept measurement error models.
J Appl Stat
; 51(8): 1545-1569, 2024.
Article
em En
| MEDLINE
| ID: mdl-38863806
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Appl Stat
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Reino Unido