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Efficient imputation methods in case of measurement errors.
Kumar, Anoop; Bhushan, Shashi; Shukla, Shivam; Bakr, M E; Alshangiti, Arwa M; Balogun, Oluwafemi Samson.
Afiliação
  • Kumar A; Department of Statistics, Central University of Haryana, Mahendergarh, 123031, India.
  • Bhushan S; Department of Statistics, University of Lucknow, Lucknow, 226007, India.
  • Shukla S; Department of Statistics, Amity University Uttar Pradesh, Lucknow, 226028, India.
  • Bakr ME; Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
  • Alshangiti AM; Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
  • Balogun OS; Department of Computing, Faculty of Science, Forestry and Technology, University of Eastern Finland, FI-70211, Finland.
Heliyon ; 10(6): e26864, 2024 Mar 30.
Article em En | MEDLINE | ID: mdl-38510003
ABSTRACT
This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article