Class Of Ratio-type Estimators Research Articles

On The Efficiency of Ratio Estimators of Finite Population Mean Using Auxiliary Information

Ratio estimation is a technique that usages available auxiliary information which is certainly correlated with study variables. In this study, a class of ratio-type estimators of finite population means has been anticipated to solve delinquent of estimation of the population mean. Properties of anticipated estimators namely Bias & Mean Square Error were acquired up to the first order of approximation & the 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.

A new class of ratio type estimators in single- and two-phase sampling

<abstract> <p>The estimation of a certain population characteristics is required for several situations. The estimates are built so that the error of estimation is minimized. In several situations estimation of the population mean is required. Different estimators for the mean are available but, there is still room for improvement. In this paper, a new class of ratio-type estimators is proposed for the estimation of the population mean. The estimators are proposed for single- and two-phase sampling schemes. The expressions for bias and mean square error are obtained for single-phase and two-phase sampling estimators. Mathematical comparison of the proposed estimators has been achieved by using some existing single-phase and two-phase sampling estimators. Extensive simulations have been conducted to compare the proposed estimators with some available single- and two-phase sampling estimators. It has been observed that the proposed estimators are better than the existing estimators. Consequently, the proposed ratio estimators are recommended for use by the practitioners in various fields of industry, engineering and medical and physical sciences.</p> </abstract>

An improved class of robust ratio estimators by using the minimum covariance determinant estimation

In this article, ratio estimators for the population mean have suggested using the robust covariance estimation under the simple random scheme. Zaman and Bulut have developed a class of ratio-type estimators for the mean estimation by utilizing robust regression coefficients. In this paper, we extend the estimators presented in Zaman and Bulut by using minimum covariance determinant (MCD) robust covariance estimation. The mean square error (MSE) equation for the new estimators is obtained. These theoretical results are supported with the aid of numerical example and simulation based on dataset that include outliers.

A class of ratio-type estimators under two-phase sampling in the presence of auxilary variables

This paper deals with the estimation of population mean under two-phase sampling. Utilizing information on two-auxiliary variables, a class of estimators for estimating the finite population mean is proposed, and its properties, up to the first order of approximation, are studied. Various estimators are suggested as special cases of this class. The performance of the suggested estimators is compared with some contemporary estimators of population mean through numerical illustrations carried over existing datasets of some natural populations. Also, a small scale Monte Carlo simulation is carried out for the empirical comparison.

A Class of Ratio-Type Estimator Using Two Auxiliary Variables for Estimating The Population Mean With Some Known Population Parameters

In this paper, we have suggested a class of ratio type estimators with a linear combination using two auxiliary variables with some known population mean of the study variable. The bias and the mean square error of the proposed estimators are derived. We identified sub-members of the class of ratio type estimators. The condition for which the the proposed the proposed estimators perform better than the sample mean per unit, Olkin (1958) multivariate ratio, classical linear regression estimator, Singh(1965), Mohanty (1967) and Swain (2012) are derived. From the analysis, it is observed that the proposed estimators perform better than the sample mean per unit and other existing ratio type estimators considered in this study.

A class of ratio type estimators for imputing the missing values under rank set sampling

ABSTRACTIn this article we consider a class of ratio type estimators for imputing the missing value under the rank set sampling (rss) for both situations in which the population mean of the auxilia...

Improved General Class of Ratio Type Estimators

Improved General Class of Ratio Type Estimators

Improved General Class of Ratio Type Estimators

Improved General Class of Ratio Type Estimators

On improvement in estimating the population mean in simple random sampling

Kadilar and Cingi [Ratio estimators in simple random sampling, Appl. Math. Comput. 151 (3) (2004), pp. 893–902] introduced some ratio-type estimators of finite population mean under simple random sampling. Recently, Kadilar and Cingi [New ratio estimators using correlation coefficient, Interstat 4 (2006), pp. 1–11] have suggested another form of ratio-type estimators by modifying the estimator developed by Singh and Tailor [Use of known correlation coefficient in estimating the finite population mean, Stat. Transit. 6 (2003), pp. 655–560]. Kadilar and Cingi [Improvement in estimating the population mean in simple random sampling, Appl. Math. Lett. 19 (1) (2006), pp. 75–79] have suggested yet another class of ratio-type estimators by taking a weighted average of the two known classes of estimators referenced above. In this article, we propose an alternative form of ratio-type estimators which are better than the competing ratio, regression, and other ratio-type estimators considered here. The results are also supported by the analysis of three real data sets that were considered by Kadilar and Cingi.

Some improved ratio type estimators of population mean and ratio in finite population sample surveys

In this paper we present a class of ratio type estimators of the population mean and ratio in a finite population sample surveys with without replacement simple random sampling design, where information on an auxiliary variate x positively correlated with the main variate y is available. Large sample approximations to mean square errors (MSE) of these estimatorsare evaluated and their MSE's are compared with the MSE of the usual ratio estimator [ybar]R of [ybar] the population mean of y. It is shown that under certain conditions these estimators are more efficient than [ybar]R. When a prior knowledge of the value of thecoefficient of variation, cy, of y is at hand, ratio type estimator, say [ybar]1 of [ybar] is proposed. It is shown, under certain conditions, that [ybar]1 is more efficient than [ybar]R. When values of cy, cx and the population correlation coefficient ρ is at hand, then we have proposed another estimator, say [ybar]2 of [ybar], which is always better than [ybar]R as far as the efficiency i...

A Generalized Estimator for the Mean of a Finite Population Using Multi-Auxiliary Information

Abstract For estimating the mean of a finite population using information on p auxiliary characters x1, ···, xp1 a class of ratio type estimators is considered. For any function h(u1, ···, up) = h(u) where , the ratio of the simple random sample mean and the population mean of the character xi, such that h(e) = 1, ei = 1, i = 1, ···, p, and such that it satisfies the conditions (1), (2) and (3) of Section 3, the estimator considered is . Asymptotic expressions for the bias and the variance of the estimator are obtained and it has been shown that ratio estimators of the form are asymptotically no more efficient than the regression estimator.