Estimation of the Population Variance Using a Smoothing Operator Under Simple Random Sampling
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Date
2019-06
Authors
Odhiambo, Lavender Akoth
Journal Title
Journal ISSN
Volume Title
Publisher
Kenyatta University
Abstract
Variance estimation has been a major concern in sample survey theory. The problem
in estimation theory is to determine estimators that have smaller variance under a
given model speci cation. However, existing variance estimators su er from boundary
problems and outlier sensitivity. To address this, a robust variance estimate of
the ratio estimator of the population mean using a multiplicative bias correction technique
under model based approach is considered. Asymptotic properties of the robust
variance estimator are investigated. Also a comparative study of the existing variance
estimators and the derived robust variance estimator of the population mean is studied.
The results of the study show that under mild assumption, the derived variance
estimator of the population mean is asymptotically more consistent and has a better
coverage probability as compared to rival variance estimators of the population mean.
Description
A Project Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science (Statistics) in the School of Pure and Applied Sciences of Kenyatta University, June 2019