The utilization of auxiliary information during surveys increases the accuracy of estimators, thereby giving more reliable estimates of the population parameters of interest. It has been established that the presence of more than one auxiliary variables, some more robust estimators can be formed by combining different estimators like product, ratio or even regression estimators and in each case the individual estimators uses its own random variable. One of the most commonly used methods is the ratio method of estimating finite totals which is the foundation of all the other methods that use auxiliary information. In this paper, an estimator of the ratio-exponential class that uses two auxiliary variables has been proposed and its variance derived. After deriving the proposed estimator the coverage probabilities were estimated. Results showed that the interval length of the proposed estimator was narrower and tighter than that of the known Horwitz-Thompson’s estimator. Two datasets from the agricultural and environmental sectors were used in order to investigate the properties of the estimator and they gave satisfactory results. Mean squared error criteria was used to investigate the performance of the proposed estimator and in both cases it had the minimum squared error values. The analysis in these paper is of very great importance in understanding environmental and agricultural data.
Published in | International Journal of Data Science and Analysis (Volume 4, Issue 4) |
DOI | 10.11648/j.ijdsa.20180404.12 |
Page(s) | 53-57 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Auxiliary Variable, Coverage Probabilities, Precision, Predictive Approach
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APA Style
Damaris Felistus Mulwa, George Otieno Orwa, Romanus Odhiambo. (2018). Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables. International Journal of Data Science and Analysis, 4(4), 53-57. https://doi.org/10.11648/j.ijdsa.20180404.12
ACS Style
Damaris Felistus Mulwa; George Otieno Orwa; Romanus Odhiambo. Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables. Int. J. Data Sci. Anal. 2018, 4(4), 53-57. doi: 10.11648/j.ijdsa.20180404.12
AMA Style
Damaris Felistus Mulwa, George Otieno Orwa, Romanus Odhiambo. Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables. Int J Data Sci Anal. 2018;4(4):53-57. doi: 10.11648/j.ijdsa.20180404.12
@article{10.11648/j.ijdsa.20180404.12, author = {Damaris Felistus Mulwa and George Otieno Orwa and Romanus Odhiambo}, title = {Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables}, journal = {International Journal of Data Science and Analysis}, volume = {4}, number = {4}, pages = {53-57}, doi = {10.11648/j.ijdsa.20180404.12}, url = {https://doi.org/10.11648/j.ijdsa.20180404.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180404.12}, abstract = {The utilization of auxiliary information during surveys increases the accuracy of estimators, thereby giving more reliable estimates of the population parameters of interest. It has been established that the presence of more than one auxiliary variables, some more robust estimators can be formed by combining different estimators like product, ratio or even regression estimators and in each case the individual estimators uses its own random variable. One of the most commonly used methods is the ratio method of estimating finite totals which is the foundation of all the other methods that use auxiliary information. In this paper, an estimator of the ratio-exponential class that uses two auxiliary variables has been proposed and its variance derived. After deriving the proposed estimator the coverage probabilities were estimated. Results showed that the interval length of the proposed estimator was narrower and tighter than that of the known Horwitz-Thompson’s estimator. Two datasets from the agricultural and environmental sectors were used in order to investigate the properties of the estimator and they gave satisfactory results. Mean squared error criteria was used to investigate the performance of the proposed estimator and in both cases it had the minimum squared error values. The analysis in these paper is of very great importance in understanding environmental and agricultural data.}, year = {2018} }
TY - JOUR T1 - Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables AU - Damaris Felistus Mulwa AU - George Otieno Orwa AU - Romanus Odhiambo Y1 - 2018/11/14 PY - 2018 N1 - https://doi.org/10.11648/j.ijdsa.20180404.12 DO - 10.11648/j.ijdsa.20180404.12 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 53 EP - 57 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20180404.12 AB - The utilization of auxiliary information during surveys increases the accuracy of estimators, thereby giving more reliable estimates of the population parameters of interest. It has been established that the presence of more than one auxiliary variables, some more robust estimators can be formed by combining different estimators like product, ratio or even regression estimators and in each case the individual estimators uses its own random variable. One of the most commonly used methods is the ratio method of estimating finite totals which is the foundation of all the other methods that use auxiliary information. In this paper, an estimator of the ratio-exponential class that uses two auxiliary variables has been proposed and its variance derived. After deriving the proposed estimator the coverage probabilities were estimated. Results showed that the interval length of the proposed estimator was narrower and tighter than that of the known Horwitz-Thompson’s estimator. Two datasets from the agricultural and environmental sectors were used in order to investigate the properties of the estimator and they gave satisfactory results. Mean squared error criteria was used to investigate the performance of the proposed estimator and in both cases it had the minimum squared error values. The analysis in these paper is of very great importance in understanding environmental and agricultural data. VL - 4 IS - 4 ER -