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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Main Authors: Housila P. Singh, Prem Chandra, Inderjit Singh Grewal, Sarjinder Singh, Cheng C. Chen, Stephen A. Sedory, Jong-Min Kim
Format: Article
Language:English
Published: University of Bologna 2013-05-01
Series:Statistica
Online Access:http://rivista-statistica.unibo.it/article/view/3658
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spelling 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
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