ESTIMATION OF MEAN IN PRESENCE OF MISSING DATA UNDER TWO-PHASE SAMPLING SCHEME

  • Narendra Singh Thakur Centre for Mathematical Sciences (CMS), Banasthali University, Rajasthan
  • Kalpana Yadav Centre for Mathematical Sciences (CMS), Banasthali University, Rajasthan
  • Sharad Pathak Department of Mathematics and Statistics, Dr. H. S. Gour Central Univesity, Sagar (M.P.)
Keywords: Estimation, Missing data, Bias, Mean squared error (M.S.E), Two-phase sampling, SRSWOR, Large sample approximations

Abstract

To estimate the population mean with imputation i.e. the technique of substituting missing data, there are a number of techniques available in literature like Ratio method of imputation, Compromised method of imputation, Mean method of imputation, Ahmed method of imputation, F-T method of imputation, and so on. If population mean of auxiliary information is unknown then these methods are not useful and the two-phase sampling is used to obtain the population mean. This paper presents some imputation methods of for missing values in twophase sampling. Two different sampling designs in two-phase sampling are compared under imputed data. The bias and m.s.e of suggested estimators are derived in the form of population parameters using the concept of large sample approximation. Numerical study is performed over two populations using the expressions of bias and m.s.e and efficiency compared with Ahmed estimators.

 

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Published
2011-11-28
Section
Articles