This paper proposes a statistical method called ‘the G method’ to highlight its generalized nature for the determination and assignment of ranks to sample observations drawn from several populations for possible use in further analyses. The sampled populations may be measurements on as low as the ordinal scale and need not be continuous or even numeric. The proposed rank determination statistical model intrinsically and structurally provides for the breaking of possible ties between sample observations and automatically assigning such observations their mean ranks. This approach and hence the proposed model therefore obviate the need for the sampled populations to be continuous. They may be discrete or even non-numeric measurements on as low as the ordinal scale. The proposed method is of more generalized and wider applicability than an existing formulation which can be used with only continuous populations and is easier to use in practice than the usual traditional method which is often tedious and cumbersome, especially with large samples. The proposed method is illustrated with some data and shown to yield the same results as other existing methods where these methods are equally applicable.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 3, Issue 1) |
| DOI | 10.11648/j.ajtas.20140301.13 |
| Page(s) | 18-24 |
| 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), 2014. Published by Science Publishing Group |
Tied Observation, Rank, Robust, Nonparametric, Rank-Order, G-Method
| [1] | Wikipedia (November, 2013): Ranking the tied encyclopedia. |
| [2] | Gibbons, J.D. (1973): Non- Parametric Statistics. An Introduction; Newbury Park: Sage Publication |
| [3] | Hollander, M. and Wolfe, D.A. (1999): Non-Parametric Statistical Methods, (2nd Edition). Wiley Inter-science, New York |
| [4] | Siegel S. (1956):Nonparametric Statistics for the Behavioural Sciences. Int. Student Edition. McGraw-Hill Kogakusha Ltd, Tokyo. |
APA Style
Oyeka Ikewelugo Cyprian Anaene., Nwankwo Chike H., Awopeju K. Abidemi. (2014). The G-Method for Rank Determination in Rank Order Statistics for ‘C’ Samples. American Journal of Theoretical and Applied Statistics, 3(1), 18-24. https://doi.org/10.11648/j.ajtas.20140301.13
ACS Style
Oyeka Ikewelugo Cyprian Anaene.; Nwankwo Chike H.; Awopeju K. Abidemi. The G-Method for Rank Determination in Rank Order Statistics for ‘C’ Samples. Am. J. Theor. Appl. Stat. 2014, 3(1), 18-24. doi: 10.11648/j.ajtas.20140301.13
AMA Style
Oyeka Ikewelugo Cyprian Anaene., Nwankwo Chike H., Awopeju K. Abidemi. The G-Method for Rank Determination in Rank Order Statistics for ‘C’ Samples. Am J Theor Appl Stat. 2014;3(1):18-24. doi: 10.11648/j.ajtas.20140301.13
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Download@article{10.11648/j.ajtas.20140301.13,
author = {Oyeka Ikewelugo Cyprian Anaene. and Nwankwo Chike H. and Awopeju K. Abidemi},
title = {The G-Method for Rank Determination in Rank Order Statistics for ‘C’ Samples},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {3},
number = {1},
pages = {18-24},
doi = {10.11648/j.ajtas.20140301.13},
url = {https://doi.org/10.11648/j.ajtas.20140301.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20140301.13},
abstract = {This paper proposes a statistical method called ‘the G method’ to highlight its generalized nature for the determination and assignment of ranks to sample observations drawn from several populations for possible use in further analyses. The sampled populations may be measurements on as low as the ordinal scale and need not be continuous or even numeric. The proposed rank determination statistical model intrinsically and structurally provides for the breaking of possible ties between sample observations and automatically assigning such observations their mean ranks. This approach and hence the proposed model therefore obviate the need for the sampled populations to be continuous. They may be discrete or even non-numeric measurements on as low as the ordinal scale. The proposed method is of more generalized and wider applicability than an existing formulation which can be used with only continuous populations and is easier to use in practice than the usual traditional method which is often tedious and cumbersome, especially with large samples. The proposed method is illustrated with some data and shown to yield the same results as other existing methods where these methods are equally applicable.},
year = {2014}
}
TY - JOUR T1 - The G-Method for Rank Determination in Rank Order Statistics for ‘C’ Samples AU - Oyeka Ikewelugo Cyprian Anaene. AU - Nwankwo Chike H. AU - Awopeju K. Abidemi Y1 - 2014/01/10 PY - 2014 N1 - https://doi.org/10.11648/j.ajtas.20140301.13 DO - 10.11648/j.ajtas.20140301.13 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 18 EP - 24 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20140301.13 AB - This paper proposes a statistical method called ‘the G method’ to highlight its generalized nature for the determination and assignment of ranks to sample observations drawn from several populations for possible use in further analyses. The sampled populations may be measurements on as low as the ordinal scale and need not be continuous or even numeric. The proposed rank determination statistical model intrinsically and structurally provides for the breaking of possible ties between sample observations and automatically assigning such observations their mean ranks. This approach and hence the proposed model therefore obviate the need for the sampled populations to be continuous. They may be discrete or even non-numeric measurements on as low as the ordinal scale. The proposed method is of more generalized and wider applicability than an existing formulation which can be used with only continuous populations and is easier to use in practice than the usual traditional method which is often tedious and cumbersome, especially with large samples. The proposed method is illustrated with some data and shown to yield the same results as other existing methods where these methods are equally applicable. VL - 3 IS - 1 ER -
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