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IJSTR >> Volume 3- Issue 11, November 2014 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Estimation Of Population Total Using Model-Based Approach: A Case Of HIV/AIDS In Nakuru Central District, Kenya

[Full Text]

 

AUTHOR(S)

Langat Reuben Cheruiyot, Tonui Benard Cheruiyot, Lagat Janet Jepchumba

 

KEYWORDS

Index Terms: Model- based approach, design -based approach, simple random sampling, stratified sampling, HIV/AIDS.

 

ABSTRACT

Abstract: In this study we have explored an estimator for finite population total under the famous prediction approach. This approach has been compared with design-based approach using simple random sampling and stratified random sampling techniques. It is shown that the estimators under model based approach give better estimates than the estimators under design based approach both when using simple random sampling (s.r.s) and stratified random sampling. The relative absolute error from both approaches is computed and has been shown to be superior under the super population model than the design based approach. This approach is then applied to predict the total number of people living with HIV/AIDS in Nakuru Central district.

 

REFERENCES

[1] Brewer, K (2002). Combined survey sampling inference. London, Arnold a member of the Hodder Headline Group.

[2] Breidt, F. J. and Opsomer, J.D. (2000). Local Polynomial Regression Estimation in Survey Sampling. Annals of Statistics, 28, 1026.

[3] Chambers, R.L. (2003). Which Sample Survey Strategy? A Review of Three Different Approaches. Southampton Statistical Sciences Research Institute. University Of Southampton.

[4] Cochran W.G. (1977). Sampling Techniques (3rd ed.). New York: John Wiley and sons.

[5] Cornfield (1944). On samples from finite populations. Journal of the American Statistics Association.39:236-239.

[6] Hansen, M.H., Madow, W.G. and Tepping, B.J. (1983). An evaluation of model dependent and probability sampling inference in sample surveys. Journal of the American statistical Associations, 78,776-807

[7] Horvitz, D. G. & Thompson, D. J. (1952). ‘A Generalization of Sampling Without Replacement from a Finite Universe’, Journal of the American Statistical Association 47, 663–685.

[8] National AIDS Control Council (2014). Kenya AIDS Response Progress Report,2014 Progress towards Zero .

[9] Rao, J.N.K, (2006). Interplay Between Sample Survey Theory and Practice: An Appraisal. Statistics Canada. Business survey Methods. 31. 2,117-138

[10] Rao, J.N.K (1996). Development in sample survey theory. The Canadian journal of statistics, 25, 1-21

[11] Royall R.M, and Cumberland W.G (1981). Prediction models and unequal probability sampling. Journal of the American Statistical Association.

[12] Särndal, C.E Swesson, B and Wretman, J.N (1992). Model- Assisted Survey sampling. New York: Springer Verlag.

[13] Sharon L.L. (2009). Sampling: Design and Analysis 2nd Ed. Richard Stratton,US.

[14] Wu, C, and Sitter, R.R. (2001). A Model Calibration Approach to Using Complete Auxiliary Information from Survey Data. Journal of American Statistical Association.