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Prediction of a Function of Misclassified Binary Data

100%
Al-Kandari N. M.
Statistics in Transition new series
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2016
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vol. 17
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issue 3
429-448
EN
We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the misclassification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a biascorrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
2
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A Class of Chain Ratio-Cum-Dual to Ratio Type Estimator with Two Auxiliary Characters Under Double Sampling in Sample Surveys

100%
Choudhury S., Singh B. K.
Statistics in Transition new series
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2012
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vol. 13
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issue 3
519-536
EN
This paper considers a chain ratio-cum-dual to ratio type estimator for estimating population mean of the study variate using two auxiliary variates under double (two-phase) sampling procedure, when the information on another additional auxiliary variate is available along with the main auxiliary variate. The asymptotically optimum estimators (AOEs) in the class are identified in two different cases with their bias and variances. The optimum values of the first phase and second phase sample sizes have been obtained for the fixed cost of survey. Theoretical and empirical studies have also been done to demonstrate the efficiency of the proposed estimator with respect to strategies which utilized the information on two auxiliary variates.
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Missing data estimation based on the chaining technique in survey sampling

84%
Singh Thakur N., Shukla D.
Statistics in Transition new series
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2022
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vol. 23
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issue 4
91-111
EN
Sample surveys are often affected by missing observations and non-response caused by the respondents' refusal or unwillingness to provide the requested information or due to their memory failure. In order to substitute the missing data, a procedure called imputation is applied, which uses the available data as a tool for the replacement of the missing values. Two auxiliary variables create a chain which is used to substitute the missing part of the sample. The aim of the paper is to present the application of the Chain-type factor estimator as a means of source imputation for the non-response units in an incomplete sample. The proposed strategies were found to be more efficient and bias-controllable than similar estimation procedures described in the relevant literature. These techniques could also be made nearly unbiased in relation to other selected parametric values. The findings are supported by a numerical study involving the use of a dataset, proving that the proposed techniques outperform other similar ones.
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