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

Autor:	Langat Reuben Cheruiyot, Odhiambo Romanus Otieno, George Otieno Orwa
Rok vydání:	2019
Předmět:	Statistics::Theory education.field_of_study Population Nonparametric statistics Kernel smoother Applied mathematics Estimator Ratio estimator education Nonparametric regression Mathematics Horvitz–Thompson estimator Parametric statistics
Zdroj:	International Journal of Statistics and Probability. 8:83
ISSN:	1927-7040 1927-7032
DOI:	10.5539/ijsp.v8n3p83
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::25039d784b8973acc60b6cd210dd5056 https://doi.org/10.5539/ijsp.v8n3p83 Zobrazit plný text záznamu