Mean Estimation for Time-Based Surveys Using Memory-Type Logarithmic Estimators

Shashi Bhushan, Anoop Kumar (), Amer Ibrahim Al-Omari and Ghadah A. Alomani
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Shashi Bhushan: Department of Statistics, University of Lucknow, Lucknow 226007, India
Anoop Kumar: Department of Statistics, Amity University, Lucknow 226028, India
Amer Ibrahim Al-Omari: Department of Mathematics, Faculty of Science, Al al-Bayt University, Mafraq 25113, Jordan
Ghadah A. Alomani: Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Mathematics, 2023, vol. 11, issue 9, 1-14

Abstract: This article examines the issue of population mean estimation utilizing past and present data in the form of an exponentially weighted moving average (EWMA) statistic under simple random sampling (SRS). We suggest memory-type logarithmic estimators and derive their properties, such as mean-square error (MSE) and bias up to a first-order approximation. Using the EWMA statistic, the conventional and novel memory-type estimators are compared. Real and artificial populations are used as examples to illustrate the theoretical findings. According to the empirical findings, memory-type logarithmic estimators are superior to the conventional mean estimator, ratio estimator, product estimator, logarithmic-type estimator, and memory-type ratio and product estimators.

Keywords: mean-square error; exponentially weighted moving average; memory-type estimators (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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