Business Survey Methods

Cox, Brenda G.; Binder, David A.; Chinnappa, B.; Christianson, Anders; Colledge, Michael; Kott, Phillip S.

doi:10.1002/9781118150504

Cited by 35 publications

(1 citation statement)

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“…It is for this reason that many researchers use nonparametric approach. They include (Dorfman, 1992), (Chambers, Dorfman, & Wehrly, 1993), (Odhiambo & Mwalili, 2000), (Tsybakov, 2009), (Chandran & Prajneshu, 2004), and (Breidt & Opsomer, 2009) among others.…”

Section: Nonparametric Regression Estimationmentioning

confidence: 99%

A Boundary Corrected Non-Parametric Regression Estimator for Finite Population Total

Cheruiyot¹,

Otieno²,

Orwa³

2019

IJSP

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This study explores the estimation of finite population total. For many years design-based approach dominated the scene in statistical inference in sample surveys. The scenario has since changed with emergence of the other approaches (Model-Based, Model-Assisted and the Randomization-Assisted), which have proved to rival the conventional approach. This paper focuses on a model based approach. Within this framework a nonparametric regression estimator for finite population total is developed. The nonparametric technique has been found from previous studies to be advantageous than its parametric counterpart in terms of robustness and flexibility.  Kernel smoother has been used in construction of the estimator. The challenge of the boundary problem encountered with the Nadaraya-Watson estimator has been addressed by modifying it using reflection technique. The performance of the proposed estimator has been compared to the design-based Horvitz Thompson estimator and the model –based nonparametric regression estimator proposed by (Dorfman, 1992) and the ratio estimator using simulated data.

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