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Jambulingam Subramani - One of the best experts on this subject based on the ideXlab platform.

  • Skewness, Kurtosis and Correlation Coefficient
    2016
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    Consider the two parameter modified ratio estimators for the estimation of finite population mean using the skewness, kurtosis and correlation coefficient of two auxiliary variables. The efficiencies of the proposed modified ratio estimators are assessed with that of the simple random sampling without replacement (SRSWOR) Sample mean and some of the existing ratio estimators in terms of mean squared errors. The entire above i

  • A Modified Approach in Linear Regression Estimator in Simple Random Sampling
    The Journal of Advanced Research in Applied Mathematics, 2016
    Co-Authors: Jambulingam Subramani
    Abstract:

    The present article deals with the estimation of finite population mean of the study variable  by introducing a modified form of linear regression estimator. The performance of the proposed modified linear regression estimator is assessed with that of simple random sampling without replacement (SRSWOR) Sample mean, ratio estimator, some of the modified linear regression estimators and linear regression estimator for certain natural population available in the literature. Further the optimum value of mean squared error of the proposed estimator is also obtained.

  • Two Parameter Modified Ratio Estimators with Two Auxiliary Variables for Estimation of Finite Population Mean with Known Skewness, Kurtosis and Correlation Coefficient
    Journal of Modern Applied Statistical Methods, 2014
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    Consider the two parameter modified ratio estimators for the estimation of finite population mean using the skewness, kurtosis and correlation coefficient of two auxiliary variables. The efficiencies of the proposed modified ratio estimators are assessed with that of the simple random sampling without replacement (SRSWOR) Sample mean and some of the existing ratio estimators in terms of mean squared errors. The entire above is explained with the help of certain natural populations available in the literature.

  • Median Based Modified Ratio Estimators with Known Quartiles of an Auxiliary Variable
    Journal of Modern Applied Statistical Methods, 2014
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    New median based modified ratio estimators for estimating a finite population mean using quartiles and functions of an auxiliary variable are proposed. The bias and mean squared error of the proposed estimators are obtained and the mean squared error of the proposed estimators are compared with the usual simple random sampling without replacement (SRSWOR) Sample mean, ratio estimator, a few existing modified ratio estimators, the linear regression estimator and median based ratio estimator for certain natural populations. A numerical study shows that the proposed estimators perform better than existing estimators; in addition, it is shown that the proposed median based modified ratio estimators outperform the ratio and modified ratio estimators as well as the linear regression estimator.

  • SOME MODIFIED LINEAR REGRESSION TYPE RATIO ESTIMATORS FOR ESTIMATION OF POPULATION MEAN USING KNOWN PARAMETERS OF AN AUXILIARY VARIABLE
    2014
    Co-Authors: Jambulingam Subramani, G. Kumarapandiyan, S. Balamurali
    Abstract:

    The present paper deals with modified linear regression type ratio estimators for estimation of population mean of the study variable when the Kurtosis, Skewness, population correlation coefficient and quartiles of the auxiliary variable are known. The bias and the mean squared error of the proposed estimators are derived and are compared with that of simple random sampling without replacement (SRSWOR) Sample mean, the usual ratio estimator and the existing modified linear regression type ratio estimators. As a result, we have derived the conditions for which the proposed estimators perform better than the other existing estimators. Further the performance of the proposed estimators with that of the existing estimators are assessed for a natural population. From the numerical study it is observed that the proposed modified ratio estimators perform better than the existing estimators.

G Prabavathy - One of the best experts on this subject based on the ideXlab platform.

  • Skewness, Kurtosis and Correlation Coefficient
    2016
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    Consider the two parameter modified ratio estimators for the estimation of finite population mean using the skewness, kurtosis and correlation coefficient of two auxiliary variables. The efficiencies of the proposed modified ratio estimators are assessed with that of the simple random sampling without replacement (SRSWOR) Sample mean and some of the existing ratio estimators in terms of mean squared errors. The entire above i

  • Two Parameter Modified Ratio Estimators with Two Auxiliary Variables for Estimation of Finite Population Mean with Known Skewness, Kurtosis and Correlation Coefficient
    Journal of Modern Applied Statistical Methods, 2014
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    Consider the two parameter modified ratio estimators for the estimation of finite population mean using the skewness, kurtosis and correlation coefficient of two auxiliary variables. The efficiencies of the proposed modified ratio estimators are assessed with that of the simple random sampling without replacement (SRSWOR) Sample mean and some of the existing ratio estimators in terms of mean squared errors. The entire above is explained with the help of certain natural populations available in the literature.

  • Median Based Modified Ratio Estimators with Known Quartiles of an Auxiliary Variable
    Journal of Modern Applied Statistical Methods, 2014
    Co-Authors: Jambulingam Subramani, G Prabavathy
    Abstract:

    New median based modified ratio estimators for estimating a finite population mean using quartiles and functions of an auxiliary variable are proposed. The bias and mean squared error of the proposed estimators are obtained and the mean squared error of the proposed estimators are compared with the usual simple random sampling without replacement (SRSWOR) Sample mean, ratio estimator, a few existing modified ratio estimators, the linear regression estimator and median based ratio estimator for certain natural populations. A numerical study shows that the proposed estimators perform better than existing estimators; in addition, it is shown that the proposed median based modified ratio estimators outperform the ratio and modified ratio estimators as well as the linear regression estimator.

  • MEDIAN BASED MODIFIED RATIO ESTIMATORS WITH LINEAR COMBINATIONS OF POPULATION MEAN AND MEDIAN OF AN AUXILIARY VARIABLE
    2013
    Co-Authors: Jambulingam Subramani, G Prabavathy, E Mail
    Abstract:

    In this paper two new median based modified ratio estimators for the estimation of finite population mean using the linear combinations of population mean and median of the auxiliary variable have been proposed. The bias and mean squared error of the proposed estimators are derived and the mean squared errors are compared with that of the SRSWOR Sample mean, ratio estimator, linear regression estimator and median based ratio estimator for certain natural populations. It is observed from the numerical comparisons that the proposed median based modified ratio estimators have outperformed the existing estimators including the linear regression estimator

E Mail - One of the best experts on this subject based on the ideXlab platform.

G. Kumarapandiyan - One of the best experts on this subject based on the ideXlab platform.

  • SOME MODIFIED LINEAR REGRESSION TYPE RATIO ESTIMATORS FOR ESTIMATION OF POPULATION MEAN USING KNOWN PARAMETERS OF AN AUXILIARY VARIABLE
    2014
    Co-Authors: Jambulingam Subramani, G. Kumarapandiyan, S. Balamurali
    Abstract:

    The present paper deals with modified linear regression type ratio estimators for estimation of population mean of the study variable when the Kurtosis, Skewness, population correlation coefficient and quartiles of the auxiliary variable are known. The bias and the mean squared error of the proposed estimators are derived and are compared with that of simple random sampling without replacement (SRSWOR) Sample mean, the usual ratio estimator and the existing modified linear regression type ratio estimators. As a result, we have derived the conditions for which the proposed estimators perform better than the other existing estimators. Further the performance of the proposed estimators with that of the existing estimators are assessed for a natural population. From the numerical study it is observed that the proposed modified ratio estimators perform better than the existing estimators.

  • A CLASS OF MODIFIED LINEAR REGRESSION ESTIMATORS FOR ESTIMATION OF FINITE POPULATION MEAN
    2012
    Co-Authors: Jambulingam Subramani, G. Kumarapandiyan
    Abstract:

    In recent times, a large number of modified ratio estimators are introduced by assuming various population parameters are known. In the same direction we have suggested a class of modified linear regression estimators which are unbiased. We have derived their variances together with the values for which the proposed class of estimators perform better than the usual linear regression estimator and existing modified ratio type estimators. Further we have shown that the estimators from SRSWOR Sample and the linear regression estimator are the particular cases of the proposed estimators. The performances of these proposed estimators are also assessed with that of linear regression estimator and some of the existing ratio type estimators for certain natural populations available in the literature. It is observed from the numerical comparisons that the proposed estimators perform better than the existing estimators and linear regression estimator.

Subramani J. - One of the best experts on this subject based on the ideXlab platform.