Local Polynomial Regression Estimator of the Finite Population Total Under Stratified Random Sampling: A Model-Based Approach

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dc.contributor.author Syengo, Charles Kilunda
dc.date.accessioned 2018-02-12T11:31:58Z
dc.date.available 2018-02-12T11:31:58Z
dc.date.issued 2018-02-12
dc.identifier.uri http://hdl.handle.net/123456789/4042
dc.description Master of Science in Mathematics (Statistics Option) en_US
dc.description.abstract In this thesis, auxiliary information is used to determine an estimator of nite population total using nonparametric regression under strati ed random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. From the simulation experiments, the resulting estimator records better results in the estimation of the nite population total. Generally, use of the proposed estimator leads to relatively smaller values of relative e ciency compared to other estimators. en_US
dc.description.sponsorship Prof. Romanus Odhiambo Otieno Jomo Kenyatta University of Agriculture and Technology Dr. George Otieno Orwa Jomo Kenyatta University of Agriculture and Technology en_US
dc.language.iso en en_US
dc.publisher JKUAT-PAUSTI en_US
dc.subject Local Polynomial en_US
dc.subject Regression Estimator en_US
dc.subject Stratified Random Sampling en_US
dc.subject Model-Based Approach en_US
dc.title Local Polynomial Regression Estimator of the Finite Population Total Under Stratified Random Sampling: A Model-Based Approach en_US
dc.type Thesis en_US


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