Browsing by Author "Singh, R. V. K."

Now showing 1 - 2 of 2
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    CLASS OF RATIO ESTIMATORS WITH KNOWN FUNCTIONS OF AUXILIARY VARIABLE FOR ESTIMATING FINITE POPULATION VARIANCE
    (Asian Journal of Mathematics and Computer Research, 2016) Audu, A.; Adewara, A. A.; Singh, R. V. K.
    In this paper, we have suggested a class of improved ratio estimators for finite population variance. The proposed class of estimators is obtained by using unknown weight on some existing estimators. The MSE of the proposed estimators have been obtained and the conditions for their efficiency over some existing variance estimators have been established. The present proposed family of finite variance estimators, having obtaining the optimal values of the constants, exhibit significant improvement over the existing estimators. The empirical study is also conducted to corroborate the theoretical results and the results show that the proposed class of estimators is more efficient.
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    RATIO AND PRODUCT TYPE EXPONENTIAL ESTIMATORS OF POPULATION VARIANCE UNDER TRANSFORMED SAMPLE INFORMATION OF STUDY AND SUPPLEMENTARY VARIABLES
    (Asian Journal of Mathematics and Computer Research, 2016) Ahmed, A.; Singh, R. V. K.; Adewara, A. A.
    In this paper, we have suggested a class of modified ratio and product exponential estimators for finite population variance. The proposed class of exponential estimators was obtained by transforming both the sample variances and means of study and auxiliary variables of Singh et al. [1] and Asgha et al. [2] estimators. The mean square errors (MSEs) of the proposed estimators have been obtained and the conditions for their efficiency over some existing variance estimators have been established. The efficiency of proposed estimators based on optimal values of the constants, exhibit significant improvement over the estimators considered in the study. The numerical illustration was also conducted to corroborate the theoretical results. The result of the empirical study does not only show high efficiency of the proposed class of estimators over existing estimators but also save cost because their efficiency is high for small samples