Estimation of Population Variance Under an Imputation Method in Two-Phase Sampling
- 36 Downloads
In this paper, an attempt has been made to reduce the negative effect of random non-response in the estimation procedure of population variance in two-phase sampling. A difference-type imputation method has been considered to reduce the wrong impact of random non-response in the two-phase sampling. To build efficient estimation strategies, information on two auxiliary characters has been used in the estimation of population variance and describes the effectiveness of the proposed estimators; dominant performances of the suggested estimators are compared with the well-known estimators of the population variance under the complete response. Results are explained through empirical studies which are followed by suitable recommendations.
KeywordsTwo-phase Imputation Random non-response Variance estimation Auxiliary variable Bias Mean square error
Mathematics Subject Classification62D05
The authors are grateful to National Institute of Technology Raipur, Chhattisgarh, and Sri Venkateswara College, University of Delhi, for providing the financial assistance and necessary infrastructure to carry out the present work.
- 2.Srivstava SK, Jhaji HS (1980) A class of estimators using auxiliary information for estimating finite population variance. Sankhya, C 42:87–96Google Scholar
- 5.Upadhyaya LN, Singh HP (1983) Use of auxiliary information in the estimation of population variance. Math Forum 6(2):33–36Google Scholar
- 8.Ahmed MS, Abu-Dayyeh W, Hurairah AAO (2003) Some estimators for population variance under two phase sampling. Stat Transit 6(1):143–150Google Scholar
- 11.Singh GN, Priyanka K, Prasad S, Singh S, Kim JM (2013) A class of estimators for population variance in two occasion rotation patterns. Commun Stat Appl Methods 20(4):247–257Google Scholar
- 13.Sande IG (1979) A personal view of hot-deck imputation procedures. Surv Methodol 5:238–247Google Scholar
- 14.Lee H, Rancourt E, Sarndall CE (1994) Experiments with variance estimation from survey data with imputed values. J Off Stat 10(3):231–243Google Scholar
- 15.Heitzan DF, Basu S (1996) Distinguish ‘missing at random’ and ‘missing completely at random. Am Stat 50:207–217Google Scholar
- 22.Ministry of Statistics & Programme Implementation (2013) Chapter-2(2.1). http://mospi.nic.in/MospiNew/upload/SYB2013/ch2.html