Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse
In this paper, a family of estimators of population ratio R , product P and mean Y0 has been suggested using multi-auxiliary information under simple random sampling without replacement (SRSWOR) and its properties have been discussed. We have further suggested three families of estimators in the pre...
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doaj-df77e6d466eb4dd0ac24ab04bcf7fe562020-11-24T23:13:39ZengUniversity of BolognaStatistica0390-590X1973-22012013-05-0172444948010.6092/issn.1973-2201/36583404Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponseHousila P. Singh0Prem Chandra1Inderjit Singh Grewal2Sarjinder Singh3Cheng C. Chen4Stephen A. Sedory5Jong-Min Kim6Vikram UniversityAll India Institute Medical Sciences, New DelhiPunjab Agricultural UniversityTexas A&M University - KingsvilleTexas A&M University - KingsvilleTexas A&M University - KingsvilleUniversity of Minnesota - MorrisIn this paper, a family of estimators of population ratio R , product P and mean Y0 has been suggested using multi-auxiliary information under simple random sampling without replacement (SRSWOR) and its properties have been discussed. We have further suggested three families of estimators in the presence of random non-response in different situations under an assumption that the number of sampling units on which information cannot be obtained due to random non-response follows some distribution. The estimators of the family involve unknown constants whose optimum values depend on unknown population parameters. When these population parameters are replaced by their consistent estimates, the resulting estimators are shown to have the same asymptotic mean squared error (MSE). The work of Singh et al. (2007) is shown as a special case. At the end, numerical comparisons are also made.http://rivista-statistica.unibo.it/article/view/3658 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Housila P. Singh Prem Chandra Inderjit Singh Grewal Sarjinder Singh Cheng C. Chen Stephen A. Sedory Jong-Min Kim |
spellingShingle |
Housila P. Singh Prem Chandra Inderjit Singh Grewal Sarjinder Singh Cheng C. Chen Stephen A. Sedory Jong-Min Kim Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse Statistica |
author_facet |
Housila P. Singh Prem Chandra Inderjit Singh Grewal Sarjinder Singh Cheng C. Chen Stephen A. Sedory Jong-Min Kim |
author_sort |
Housila P. Singh |
title |
Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
title_short |
Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
title_full |
Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
title_fullStr |
Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
title_full_unstemmed |
Estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
title_sort |
estimation of population ratio, product, and mean using multiauxiliary information with random nonresponse |
publisher |
University of Bologna |
series |
Statistica |
issn |
0390-590X 1973-2201 |
publishDate |
2013-05-01 |
description |
In this paper, a family of estimators of population ratio R , product P and mean Y0 has been suggested using multi-auxiliary information under simple random sampling without replacement (SRSWOR) and its properties have been discussed. We have further suggested three families of estimators in the presence of random non-response in different situations under an assumption that the number of sampling units on which information cannot be obtained due to random non-response follows some distribution. The estimators of the family involve unknown constants whose optimum values depend on unknown population parameters. When these population parameters are replaced by their consistent estimates, the resulting estimators are shown to have the same asymptotic mean squared error (MSE). The work of Singh et al. (2007) is shown as a special case. At the end, numerical comparisons are also made. |
url |
http://rivista-statistica.unibo.it/article/view/3658 |
work_keys_str_mv |
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