In scientific analyses, datasets commonly incorporate measurement errors. Disregarding these errors can introduce bias to the estimates and compromise the accuracy of the conclusions drawn. In this context, we focus on estimating the extropy function while accounting for measurement errors. Two distinct estimators for the extropy function are put forth, and their asymptotic properties are derived. To assess the accuracy of these estimators, a simulation study is conducted. Furthermore, the precision of the estimators is validated through an examination of real data.

Estimation of extropy function in the presence of measurement error / Irshad, M. R.; Archana, K.; Maya, R.; Longobardi, M.. - In: METRIKA. - ISSN 0026-1335. - 88:(2025), pp. 955-979. [10.1007/s00184-024-00979-9]

Estimation of extropy function in the presence of measurement error

Longobardi, M.
2025

Abstract

In scientific analyses, datasets commonly incorporate measurement errors. Disregarding these errors can introduce bias to the estimates and compromise the accuracy of the conclusions drawn. In this context, we focus on estimating the extropy function while accounting for measurement errors. Two distinct estimators for the extropy function are put forth, and their asymptotic properties are derived. To assess the accuracy of these estimators, a simulation study is conducted. Furthermore, the precision of the estimators is validated through an examination of real data.
2025
Estimation of extropy function in the presence of measurement error / Irshad, M. R.; Archana, K.; Maya, R.; Longobardi, M.. - In: METRIKA. - ISSN 0026-1335. - 88:(2025), pp. 955-979. [10.1007/s00184-024-00979-9]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/985793
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