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

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Listed:
  • Langat Reuben Cheruiyot
  • Odhiambo Romanus Otieno
  • George O. Orwa

Abstract

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.

Suggested Citation

  • Langat Reuben Cheruiyot & Odhiambo Romanus Otieno & George O. Orwa, 2019. "A Boundary Corrected Non-Parametric Regression Estimator for Finite Population Total," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(3), pages 1-83, November.
    • Handle: RePEc:ibn:ijspjl:v:8:y:2019:i:3:p:83
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    References listed on IDEAS

    as
    1. Masayuki Hirukawa & Mari Sakudo, 2015. "Family of the generalised gamma kernels: a generator of asymmetric kernels for nonnegative data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(1), pages 41-63, March.
    2. M. Luz Gámiz & K. B. Kulasekera & Nikolaos Limnios & Bo Henry Lindqvist, 2011. "Applied Nonparametric Statistics in Reliability," Springer Series in Reliability Engineering, Springer, number 978-0-85729-118-9, December.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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