dc.contributor.author Okungu, Jacob dc.contributor.author Orwa, George dc.contributor.author Odhiambo, Romanus dc.date.accessioned 2021-05-07T12:45:26Z dc.date.available 2021-05-07T12:45:26Z dc.date.issued 2019 dc.identifier.uri http://hdl.handle.net/11071/10474 dc.description Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, Kenya en_US dc.description.abstract In survey sampling, the main objective is more often than not to establish information en_US about any population parameter using the sample statistics. A nonparametric estimator of the finite population total is proposed. The nonparametric estimator of finite population total by Dorfman (1992) is developed and the coverage probabilities explored using the Edgeworth. The asymptotic properties; unbiasedness, efficiency and coverage rates of the estimator are analytically explored. In literature, a lot of work has been done on analyzing unbiasedness and efficiency of the estimators and more particularly for the population total estimators. This study departs from these studies by studying the tail properties using the confidence interval in more detail as opposed to just the unbiasedness, efficiency and mean squared error. An empirical analysis is done on three artificial functions; linear, quadratic and exponentially. It is observed that the coverage probabilities from Edgeworth expansion have higher coverage probabilities compared to design-based Horvitz-Thompson and Ratio estimators of the finite population total. The Edgeworth expansion also gave a tighter confidence interval length. dc.description.sponsorship Meru University of Science and Technology, Kenya. en_US Jomo Kenyatta University of Agriculture and Technology, Kenya. dc.language.iso en_US en_US dc.publisher Strathmore University en_US dc.subject Asymptotic normality en_US dc.subject Coverage Probability en_US dc.subject Edgeworth Expansion en_US dc.title Coverage probability of a non-parametric estimator for a finite population total using edgeworth expansion en_US dc.type Article en_US
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### This item appears in the following Collection(s)

• SIMC 2019 [99]
5th Strathmore International Mathematics Conference (August 12 – 16, 2019)