International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




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Research Paper | Statistics | Kenya | Volume 2 Issue 10, October 2013


Artificial Neural Network Model Based Estimation of Finite Population Total

Robert Kasisi [2] | Romanus O. Odhiambo | Anthony G. Waititu


Abstract: In a finite population denoted U=1, 2, 3,. . . . . , N of N identifiable units, let X be the survey variable. The survey variables are observations from a super population. It is possible to get total information about these survey variables such as their total population, mean or their variance. In most cases auxiliary information about X is provided. A simple approach to using this auxiliary information is to assume a working model that describes the connection between the study variable of interest and the auxiliary variables. Estimators are then derived on the basis of this model. The best estimators are the ones that have good efficiency if the model is true, and are consistent if the model is inappropriate. In this study, we derive a nonparametric artificial neural network estimator of finite population total. The estimator is design unbiased, design consistent and asymptotic normal.


Keywords: superpopulation, auxiliary information, artificial neural networks, sample, survey sampling


Edition: Volume 2 Issue 10, October 2013,


Pages: 237 - 241

Artificial Neural Network Model Based Estimation of Finite Population Total


How to Cite this Article?

Robert Kasisi, Romanus O. Odhiambo, Anthony G. Waititu, "Artificial Neural Network Model Based Estimation of Finite Population Total", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=16101301, Volume 2 Issue 10, October 2013, 237 - 241, #ijsrnet

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