EFFICIENCY OF RANKED SET SAMPLING IN HORTICULTURAL SURVEYS
DOI:
https://doi.org/10.12957/cadest.2015.19114Resumo
DOI: 10.12957/cadest.2015.19114
Abstract
In this paper, we explore the feasibility of using RSS (Ranked Set Sampling) in improving the estimates of the population mean in comparison to SRS (Simple Random Sampling) in Horticultural research. We use an experience developed with a survey of apples in India. The numerical results suggest that RSS procedure results in a substantial reduction of standard errors, and thus provides more efficient estimates than SRS, in the specific Horticultural Survey studied, using the same sample size. Then it is recommended as an easy-to-use accurate method to management of this Horticulture problem.
Key-words: Ranked Set Sampling, Simple Random Sampling, Standard Error, Accuracy.
Referências
Al-Omari, A. I. and C. N. Bouza (2014). Review of Ranked Set Sampling: Modifications and Applications. Revista Investigación Operacional, 35, 215-240.
Bai, Z.D. and Chen, Z.( 2003). On the theory of ranked set sampling and its ramifications.Journal of Statistical Planning and Inference109 : 81-99.
Bouza, C. (2010). Ranked set sampling for estimating of population under non-response. Revista Investigacion Operacional31 : 140-150.
Bouza, C.N. (2013). Handling Missing Data in Ranked Set Sampling, Springer Briefs in Statistics, Springer
Chen, Z. (2001). Ranked-set sampling with regression type estimators.Journal of Statistical Planning and Inference 92 : 181-192.
Chen, Z. and Bai, Z.D. (2000).The optimal ranked-set sampling scheme for parametric families.Sankhya Ser. A. 62 : 178-192.
Cochran, W.G. (1977). Sampling Techniques. John Wiley and Sons, New York.
Gaajendra, K.A. and Bouza, C. (2012). Double sampling with rank set selection in the second phase with non-response: Analytical results and Monte Carlo experiments. Journal of Probability and Statistics.23 : 45-53.
Gockowski J. and M. Ndoumbé (2004): The adoption of intensive monocrop horticulture in southern Cameroon. Agricultural Economics.30, 195–202
Jeelani, M.I., Mir, S.A., Khan,I.,Nazir,N and Jeelani,F.(2014). Non-response problems in ranked set sampling. Pakistan Journal of Statistics. 30(4), 555-562.
Kaur, A., Patil, G.P. and Taillie, C. (1997). Unequal allocation models for ranked set sampling with skew distributions. Biometrics 53 : 123-130.
Martin, W.L., Shank, T.L., Oderwald, R.G. and Smith, D.W. (1980). Evaluation of ranked set sampling for estimating shrub phytomass in Appalachian Oak forest.Technical Report No.FWS-4-80, School of Forestry and Wildlife Resources VPI & SU Blacksburg, VA.
McIntyre, G.A. (1952). A Method for unbiased selective sampling, using ranked sets. Australia Journal of Agric. Res. 3 : 385-390.
Ozkan, B., A. Kurklu and H. Akcaoz (2004): An input–output energy analysis in greenhouse vegetable production: a case studyfor Antalya region of Turkey. Biomass and Bioenergy, 26, 89–95.
Ozturk and Wolfe, D.A. (1998). Optimal ranked set sampling protocol for the signed rank test. Technical Report TR 630, Ohio State University Department of Statistics.
Risch, N. and Zhang, H. (1995). Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science. 268 : 1584-1589.
