1,720,973 research outputs found
A class of estimators in two-phase sampling with subsampling the non-respondents
No full textMotivated by Singh and Kumar [14], we introduce in this paper a general class of estimators for the population mean of a study variable when two auxiliary variables are used in the presence of non-response. The minimum asymptotic variance bound of the estimators belonging to the class is determined and the optimality of Singh-Kumar estimators discussed. The best estimator in the class is analytically found in accordance with the auxiliary information used and the efficiency gain that can be achieved upon competitive estimators is shown by an empirical study. © 2013 Elsevier Inc. All rights reserved
Estimating a sensitive proportion through randomized response procedures based on auxiliary information
No full textRandomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity and reduce nonrespondents
rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many ingenious devices have been suggested to increase respondent’s
privacy protection and to better estimate the proportion of people, πA, bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation
of non-sensitive parameters, very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving from
Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for πA, for a generic randomization scheme, when the mean
of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also identified. We prove that
the best estimator acts as a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the auxiliary variable. The
general results are then applied to Warner and Simmons’ model
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