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dc.contributor.advisorArnab, R.
dc.contributor.advisorNorth, Delia Elizabeth.
dc.contributor.authorHargovan, Kashmira Ansuyah.
dc.date.accessioned2010-08-20T12:34:22Z
dc.date.available2010-08-20T12:34:22Z
dc.date.created2007
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/10413/443
dc.descriptionThesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2007.en_US
dc.description.abstractIn this thesis, we have considered the inference aspects of sampling from a finite population. There are significant differences between traditional statistical inference and finite population sampling inference. In the case of finite population sampling, the statistician is free to choose his own sampling design and is not confined to independent and identically distributed observations as is often the case with traditional statistical inference. We look at the correspondence between the sampling design and the sampling scheme. We also look at methods used for drawing samples. The non – existence theorems (Godambe (1955), Hanurav and Basu (1971)) are also discussed. Since the minimum variance unbiased estimator does not exist for infinite populations, a number of estimators need to be considered for estimating the same parameter. We discuss the admissible properties of estimators and the use of sufficient statistics and the Rao-Blackwell Theorem for the improvement of inefficient inadmissible estimators. Sampling strategies using auxiliary information, relating to the population, need to be used as no sampling strategy can provide an efficient estimator of the population parameter in all situations. Finally few well known sampling strategies are studied and compared under a super population model.en_US
dc.language.isoenen_US
dc.subjectSampling (Statistics)en_US
dc.subjectPrediction theory.en_US
dc.subjectTheses--Statistics and actuarial science.en_US
dc.titleInference from finite population sampling : a unified approach.en_US
dc.typeThesisen_US


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