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Synthetic Estimators Using Auxiliary Information in Small Domains

100%
Rai P. K., Pandey K. K.
Statistics in Transition new series
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2013
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vol. 14
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issue 1
31-44
EN
In the present article we discuss the generalized class of synthetic estimators for estimating the population mean of small domains under the information of two auxiliary variables, and describe the special cases under the different values of the constant beta involved in the proposed generalized class of synthetic estimator. In addition we have taken a numerical illustration for the two auxiliary variables and compared the result for the synthetic ratio estimator under single and two auxiliary variables.
2
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Small Area Prediction under Alternative Model Specifications

88%
Erciulescu A. L., Fuller W. A.
Statistics in Transition new series
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2016
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vol. 17
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issue 1
9-24
EN
Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples.
3
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Best Linear Unbiased Estimators of Population Mean on Current Occasion in Two-Occasion Rotation Patterns

88%
Singh G. N., Prasad S.
Statistics in Transition new series
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2013
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vol. 14
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issue 1
57-74
EN
Best linear unbiased estimators have been proposed to estimate the population mean on current occasion in two-occasion successive (rotation) sampling. Behavior of the proposed estimators have been studied and their respective optimum replacement policies are discussed. Empirical studies are carried out to examine the performance of the proposed estimators and consequently the suitable recommendations are made.
4
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Some Rotation Patterns in Two-Phase Sampling

75%
Singh G. N., Prasad S.
Statistics in Transition new series
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2011
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vol. 12
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issue 1
25-44
EN
A problem related to the estimation of population mean on the current occasion using two-phase successive (rotation) sampling on two occasions has been considered. Two-phase ratio, regression and chain-type estimators for estimating the population mean on current (second) occasion have been proposed. Properties of the proposed estimators have been studied and their respective optimum replacement policies are discussed. Estimators are compared with the sample mean estimator, when there is no matching and the natural optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample on the current occasion. Results are demonstrated through empirical means of comparison and suitable recommendations are made.
5
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Percentile-Adjusted Estimation of Poverty Indicators for Domains Under Outlier Contamination

75%
Veijanen A., Lehtonen R.
Statistics in Transition new series
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2011
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vol. 12
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issue 2
345-356
EN
Traditional estimation of poverty and inequality indicators, such as the Gini coefficient, for regions does not currently use auxiliary information or models fitted to income survey data. A predictor-type estimator constructed from ordinary mixed model predictions is not necessarily useful, as the predictions have too small spread for estimation of income statistics. Ordinary bias corrections are aimed at correcting the expectation of predictions, but poverty indicators would not be affected at all by a correction involving multiplication of predictions. We need a method improving the shape of the distribution of predictions, as poverty indicators describe differences of income between people. We therefore introduce a transformation bringing the percentiles of transformed predictions closer to the percentiles of sample values. The experiments show that the transformation results in smaller MSE of a predictor. If unit-level data from population are not available, the marginal domain frequencies of qualitative auxiliary variables can be successfully incorporated into a new calibration-based predictor-type estimator. The results are based on design-based simulation experiments where we use a population generated from an EU-wide income survey. The study is a part of the AMELI project funded by the European Union under the Seventh Framework Programme for research and technological development (FP7).
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