Variance Estimation Using Jackknife Method in Ranked Set Sampling under Finite Population Framework
| dc.contributor.advisor | Tauqueer Ahmad | |
| dc.contributor.author | ANKUR BISWAS | |
| dc.date.accessioned | 2016-12-24T13:33:20Z | |
| dc.date.available | 2016-12-24T13:33:20Z | |
| dc.date.issued | 2009 | |
| dc.description | t-8100 | en_US |
| dc.description.abstract | In experimental settings where measuring an observation is expensive, but ranking a small subset of observations is relatively easy, Ranked Set Sampling (RSS) can be used to increase the precision of the estimators. The majority of research in RSS has been concerned with estimating the mean are in the context of infinite population. Estimating the variance in case of RSS has been found to be cumbersome in the context of finite population. Therefore, in this study, an attempt was made to develop variance estimation procedures using Jackknife method in RSS under finite population framework. Under this study, three different variance estimation procedures have been developed using Jackknife method in ranked set sampling under finite population framework. The efficiency of these developed variance estimation procedures has been compared among themselves through a simulation study. The performance of variance estimation procedure following cycle based approach has been found to be at par with strata based approach for varying number of cycles as well as for varying ranks. The variance estimation procedures following cycle based approach and strata based approach have performed better than the variance estimation procedure following unit based approach for varying number of cycles as well as for varying ranks. | en_US |
| dc.identifier.uri | http://krishikosh.egranth.ac.in/handle/1/92653 | |
| dc.sub | Agricultural Statistics and Informatics | en_US |
| dc.these.type | M.Sc | |
| dc.title | Variance Estimation Using Jackknife Method in Ranked Set Sampling under Finite Population Framework | en_US |
| dc.type | Thesis | en_US |