African Journal of
Mathematics and Computer Science Research

  • Abbreviation: Afr. J. Math. Comput. Sci. Res.
  • Language: English
  • ISSN: 2006-9731
  • DOI: 10.5897/AJMCSR
  • Start Year: 2008
  • Published Articles: 252

Full Length Research Paper

Utilization of mixture of andin imputation for missing data in post-stratification

D. Shukla1, D. S. Thakur2 and N. S. Thakur3
  1Department of Mathematics and Statistics, Dr. H. S. Gour Central University, Sagar, (M.P.), India. 2School of Excellence, Sagar (M.P.), India. 3Centre for Mathematical Sciences (CMS), Banasthali University, Rajasthan, India.
Email: [email protected]

  •  Accepted: 10 January 2012
  •  Published: 15 February 2012



To estimate the population mean using auxiliary variable there are many estimators available in literature like-ratio, product, regression, dual-to-ratio estimator and so on. Suppose that all the information of the main variable is present in the sample but only a part of data of the auxiliary variable is available. Then, in this case none of the aforementioned estimators could be used. This paper presents an imputation based factor-type class of estimation strategy for population mean in presence of missing values of auxiliary variables. The non-sampled part of the population is used as an imputation technique in the proposed class. Some properties of estimators are discussed and numerical study is performed with efficiency comparison to the non-imputed estimator. An optimum sub-class is recommended.


Key words: Imputation, non-response, post-stratification, simple random sampling without replacement (SRSWOR), respondents (R).