Ratio-Cum-Product Estimators of Population Mean Using Known Population Parameters of Auxiliary Variates (original) (raw)
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2009
is an international journal published jointly by the Polish Statistical Association (PTS) and the Central Statistical Office of Poland, on a quarterly basis (during 1993-2006 it was issued twice and since 2006 three times a year). Also, it has extended its scope of interest beyond its originally primary focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy through embracing questions related to systemic transformations of and within the national statistical systems, world-wide.
Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables
Asian Journal of Research in Computer Science, 2020
A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators. Original Research Article Muili et al.; AJRCOS, 6(1): 55-65, 2020; Article no.AJRCOS.59248 56
Bayero Journal of Pure and Applied Sciences, 2017
The estimation of population mean is one of the challenging aspects in sampling theory and population study and much effort has been vigorously employed to improve the precision of estimates. In this research work, a modified rati study variable Y using median and coefficient of variation of the auxiliary variable random sampling scheme is proposed. T have been obtained under large sample approximation, asymptotically optimum estimator (AOE) is identified with its approximate MSE formula. Estimator based on "estimated optimum values" was also investigated. Theoretical and empirical comparison of proposed estimator with some other ratio and product estimators justified the performance of the proposed estimators. A minimum of 20 percent reduction in the MSE were observed from each of the existing esti is found that the proposed estimator were uniformly better than all other modified ratio and product estimators and thus most preferred over the existing estimators for the use in practical application.
2017
Use of auxiliary information has been in practice to improve the efficiency of the estimators of parameters. Ratio, product and regression methods are good examples of use of auxiliary information. Ratio, product and regression type estimators essentially require the knowledge of population mean of auxiliary variates. But many times, the information on population mean of the auxiliary variate is not available. In this type of situations, double sampling is used. Ajagaonkar (1975) and Sisodia and Dwivedi (1982) discussed problem of estimation using single auxiliary variate whereas Khan and Tripathi (1967), Rao (1975) and Singh and Namjoshi (1988) considered the use of multi auxiliary variates in double sampling. Singh (1967) used information on two auxiliary variates and envisaged a ratio-cum-product estimator of finite population mean of the study variate assuming that the population mean of the auxiliary variates are known. Upadhyaya and Singh (1999) proposed some ratio type estima...
Improved Ratio Estimators for Estimating Population Mean Using Auxiliary Information
International Journal of Scientific and Research Publications, 2020
The study presents ratios estimators for the finite population mean. The new proposed estimators are based on Subzar et al. (2018) estimators. The characteristics of the proposed estimators, i.e. bias and mean square error, were derived up to the first approximation by the Taylor series expansion, and the conditions for its effectiveness were established relative to some existing estimators. The effectiveness of the proposed estimators shows a significant improvement over the estimators considered in the study. The results of the empirical study show that the proposed estimators are more effective than existing estimators based on measurements of the comparison criteria.
Some New Functional Forms of the Ratio and the Product Estimators of the Population Mean
2020
In this paper, some new functional forms of ratio and product estimators of population mean, namely, logarithmic ratio and product estimators have been introduced. The expressions of biases and mean square errors (MSEs) of these estimators have been obtained up to order . Further, the proposed estimators have been compared with the mean per unit, usual ratio and product estimators, and it has been found that the former are more efficient than the latter under a certain set of conditions. Also, under some practical situations, biases of proposed estimators are less than the corresponding biases of exponential ratio and product type estimators. Moreover, to improve the efficiency of proposed estimators, the transformation have been considered by shifting the origin of auxiliary variable and the optimum transformations have also been found for the proposed estimators for which MSEs of these estimators become minimum. KEYWORDS— Efficiency, Ratio Estimation, Simple Random Sampling, Trans...
Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean
In this paper, some dual to ratio-cum-product estimators of population mean using known parameters of auxiliary variables are considered. These estimators are computed and compared with respect to bias and mean squared error using a simulation study from the normal population. Coefficient of skewness and coefficient of kurtosis are also computed to have an idea about the sampling distribution of dual to ratio-cum-product estimators of population mean. Dual to ratio-cumproduct estimators are more efficient than that of the mean per unit, classical ratio estimator and linear regression estimators and Choudhury and Singh (2012) estimator is more efficient estimator among the dual to ratio-cum-product estimators of population mean.
Improved Estimation of the Population Mean Using Known Parameters of an Auxiliary Variable
2011
An improved ratio-cum-product type estimator of the finite population mean is proposed using known information on the coefficient of variation of an auxiliary variate and correlation coefficient between a study variate and an auxiliary variate. Realistic conditions are obtained under which the proposed estimator is more efficient than the simple mean estimator, usual ratio and product estimators and estimators proposed by Singh and Diwivedi (
On The Efficiency of Ratio Estimators of Finite Population Mean Using Auxiliary Information
Oriental journal of physical sciences, 2022
Ratio estimation is technique that usages available auxiliary information which is certainly correlated with study variable. In this study, class of ratio-type estimators of finite population mean has been anticipated to solve delinquent of estimation of population mean. Properties of anticipated estimators namely Bias & Mean Square Error were acquired up to first order of approximation & condition for their efficiency over some existing estimators was also established. The results show that anticipated estimators are enhanced & proficient (minimum mean square errors) than other estimators with the highest precision.