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Estimation of Population Mean Using Multi-Auxiliary Characters With Subsampling The Nonrespondents

100%
Khare B. B., Sinha R. R.
Statistics in Transition new series
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2011
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vol. 12
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issue 1
45-56
EN
The aim of this paper is to suggest a class of two phase sampling estimators for population mean using multi-auxiliary characters in presence of non-response on study character. The expressions for bias and mean square error are obtained. The condition for minimum mean square error of the proposed class of estimators has been given. The optimum values of the size of first phase sample, second phase sample and the sub sampling fraction of non-responding group have been determined for the fixed cost and for the specified precision. A comparative study of the proposed class of estimators has been carried out with an empirical study.
2
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On some efficient classes of estimators using auxiliary attribute

100%
Bhushan S., Kumar A.
Statistics in Transition new series
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2023
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vol. 24
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issue 2
141-157
EN
This paper considers some efficient classes of estimators for the estimation of population mean using known population proportion. The usual mean estimator, classical ratio, and regression estimators suggested by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the suggested class of estimators. The expressions of bias and mean square errors are derived up to first-order approximation. The proposed estimators were put to test against various other competing estimators till date. It has been found both theoretically and empirically that the suggested classes of estimators dominate the existing estimators.
3
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An improved ridge type estimator for logistic regression

88%
Varathan N.
Statistics in Transition new series
|
2022
|
vol. 23
|
issue 3
113-126
EN
In this paper, an improved ridge type estimator is introduced to overcome the effect of multicollinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator. It is obtained by combining the ridge estimator and the almost unbiased ridge estimator. In order to asses the superiority of the proposed estimator over the existing estimators, theoretical comparisons based on the mean square error and the scalar mean square error criterion are presented. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimator with the existing ones. Finally, a real data example is provided to support the findings.
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