JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY This paper proposes a new product-type estimator which is complementary, in a certain sense, to the commonly used ratio estimator. Exact formulae for the bias and mean squared error can be derived which is not the case for the ratio estimator. The correction of the new estimator for bias is examined. A brief empirical study is also included.Some key words: Bias; Mean squared error; Probability sampling design; Ratio method of estimation.
SummaryThis paper examines a simple transformation which enables the use of product method in place of ratio method. The convenience with the former, proposed by Murthy [3], is that expressions for bias and mean square error (mse) can be exactly evaluated. The optimum situation in the minimum mse sense and allowable departures from this optimum are indicated. The procedure requires a good guess of a certain parameter, which does not seem very restrictive for practice. Two methods for dealing with the bias of the estimator are mentioned. An extension to use multiauxiliary information is outlined.
This paper discusses two estimators of the mean of a finite population based on a simple random sample from it, when supplementary information on a variable positively correlated with the variable of interest is available. Simultaneous reductions in absolute bias and mean square error of the estimator are seen as compared with those of the traditional estimator in the ratio method of estimation. The suggested estimators are simple for computation and there is no appreciable increase in the cost as well.
In sample surveys the ratio (product) method of estimation is quite effective when there is a positively (negatively) high correlation between the study variate and an auxiliary variate on which supplementary information is available. This paper considers four estimators suited for cases where these correlations are only moderate and gives a rule of thumb for choosing among these and the traditional estimators. Such a choice needs a good guess of the interval containing a certain parameter k, which may not be hard in survey practice. A numerical example has been included for the case of positive correlation for illustration. An extension for using multiauxiliary information is also considered.
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