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Shrinkage estimation in the gamma density using prior information is valuable in various fields, including finance, healthcare, and environmental science, where accurate parameter estimation is essential for decision-making and modeling. This manuscript considers the problem of estimation of θ α in Gamma density G(1/θ, p) when the prior estimate or guessed value of the parameter θ α is available in the form of point estimate θ 0 α . Some families of estimators of θ α are defined with its properties. Estimators developed by other authors are identified as particular members of the suggested families of shrinkage estimators. In particular, we have discussed the properties of the suggested families of estimators in an exponential distribution with known coefficient of variation. Numerical illustrations are also given in order to judge the merits of the proposed families of estimators over others. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop more effective methods for estimating population means in sample surveys using auxiliary attributes. To achieve this goal, we introduce a modified version of the estimators proposed by Koyuncu (2013b) and Shahzad et al. (2019), as well as a new class of estimators. We derive expressions for the bias and mean squared error of these new estimators up to the first degree of approximation. Our results show that the suggested classes of estimators perform better than other existing methods, with the lowest mean squared error under optimal conditions. We also conduct an empirical investigation to support our findings. [ABSTRACT FROM AUTHOR]

This manuscript considers the problem of estimating the population mean of the study variable y using information on two auxiliary attributes under simple random sampling without replacement (SRSWOR) scheme. We have defined a class of estimators for population mean based on two auxiliary attributes. We have studied the properties the suggested class of estimators up to first order of approximation. The regions of preferences are obtained under which the proposed class of estimators is more than some existing estimators including Haq and Shabir (2014) estimator. The optimum conditions are obtained at which the proposed class of estimators has the minimum mean squared error. This study is well supported through two numerical data sets. [ABSTRACT FROM AUTHOR]

In this paper we have suggested an exponential estimator of ratio cum product-type for finite population mean in double sampling for stratification. The proposed estimator's bias and mean squared error were obtained up to the first order of approximation. Comparisons have been made theoretically and numerically both with unbiased estimator, ratio and product-type estimator, ratio and product-type exponential estimators. In support of the work, an empirical study is also performed. [ABSTRACT FROM AUTHOR]

This paper suggests a general class of estimators for unknown general population parameter of the study variable using auxiliary information in sample surveys. A large number of estimators of different parameters of the study variable based on auxiliary information can be viewed as a member of the suggested class of estimators. We have derived the bias and mean squared error of the suggested class of estimators under large sample approximation. Optimum conditions are obtained for which the proposed class of estimator has the minimum mean squared error. In particular, a class of estimators for the population mean of the study variable based on the coefficient variation of the auxiliary variable has been derived. The bias and mean squared error of the proposed class of estimators are obtained under large sample approximation. It has been shown that the proposed class of estimators is better than the usual unbiased estimator y ¯ and Das and Tripathi’s (Sankhya C 42(1–2):76–86, 1980) estimator in the context of bivariate normal and bivariate non-normal populations. In support of the present study, an empirical is given. [ABSTRACT FROM AUTHOR]

In many human surveys, information is in most cases not obtained from all the units in the survey even after some call backs. To cope with this problem, survey statisticians usually consider and adopt the non-respondents sub sampling schemes introduced by Hansen and Hurwitz (1946, Jour. Amer. Statist. Assoc., 41, 517–529). The topic which is of practical importance is the estimation of population mean at the current occasion in two- occasion successive sampling in presence of non-response. In this paper, an alternative to Satici and Kadilar (2011, Hacettepe Jour. Math. Statist. 40(1), 115–124) estimator for the population mean at the current occasion in successive sampling having two occasions is suggested under the missing data case. The minimum mean squared error and optimum fraction of a fresh sample of suggested estimator are obtained. The suggested estimator is compared with the estimator due to Singh and Priyanka (2007, Statistics in Transition. 8(2), 273–292) and Satici and Kadilar (2011, Hacettepe Jour. Math. Statist. 40(1), 115–124) and the efficient conditions for the suggested estimator are obtained. It has been shown theoretically that the proposed class of estimators is more efficient than the Singh and Priyanka (2007) and Satici and Kadilar (2011) estimators. The performance of the suggested class of estimators is examined over Singh and Priyanka (2007) and Satici and Kadilar (2011) estimators with the aid of an empirical study. [ABSTRACT FROM AUTHOR]

The crux of the paper is to develop a very general class of estimators for population mean Y ¯ of the study variable y using information on auxiliary variable x in simple random sampling. It has been shown that a large number of known and unknown estimators are members of the suggested class of estimators. Expressions of bias and mean squared errors of the proposed class of estimators have been obtained under large sample approximation. Asymptotic optimum estimator (AOE) in the proposed class of estimators has been obtained with its mean squared error formula. An empirical study is carried out to demonstrate the superiority of the members of the proposed class of estimators over existing estimators. [ABSTRACT FROM AUTHOR]

The design of this paper is to examine the problem of estimation of finite population product in mail surveys for the current occasion in the context of sampling on two occasions is attempted when there is non-response on both the occasions. Estimators for the current occasion are derived as a particular case when there is non-response on first and second occasion only. The gain in efficiency of the proposed estimate over the direct estimate using no information gathered on the first occasion is computed. The proposed strategy has been compared with other estimators. An empirical study is carried out to study the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract: A class of unbiased estimators of the population variance is proposed when the coefficient of variation is known for each stratum. The technique of equilibrated stratification is used. It is shown that, with optimum allocation, the variance of the suggested estimator for sufficiently large sample size is less than that of the classical minimum variance unbiased estimator under distribution-free setting. Other classes of estimators are also given with similar or better practical properties. [Copyright &y& Elsevier]

In this paper we have suggested two modified estimators of population mean using power transformation. It has been shown that the modified estimators are more efficient than the sample mean estimator, usual ratio estimator, Sisodia and Dwivedi's (1981) estimator and Upadhyaya and Singh's (1999) estimator at their optimum conditions. Empirical illustrations are also given for examining the merits of the proposed estimators. Following Kadilar and Cingi (2003) the work has been extended to stratified random sampling, and the same data set has been studied to examine the performance in stratified random sampling. [ABSTRACT FROM AUTHOR]

In this paper, we consider chain ratio and regression type estimators for estimating median in survey sampling. We find expressions for the variance of the chain-ratio and chain-regression type estimators considered in the present investigation. The optimum values of the first phase and second phase sample sizes are also obtained for the fixed cost of survey. The relative efficiency of chain-ratio and chain-regression type estimators have been studied in comparison to ratio and regression type estimators of median proposed by Singh, Joarder and Tracy (2001). [ABSTRACT FROM AUTHOR]

This paper is devoted to the problem of estimating the square of population mean (&micro &sup 2; in normal distribution when a prior estimate or guessed value δ of the population variance δ &sup 2; is available. We have suggested a family of shrinkage estimators †[sub p], say, for µ&sup 2; with its mean squared error formula A condition is obtained in whick the suggested estimator †[sub p] is more efficient than Srivastava et al's (1980) estimator †[sub mm] Numerical illustrations have been carried out to demostrate the merits of the constructed estimator †[sub p] over †[sub mm]. It is observed that some of these estimator offer improvements over †[sub mm] particularly when the population is heterogenous and φ is in the vicinity of φ. [ABSTRACT FROM AUTHOR]