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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]

This article addresses the problem of estimating the proportion Pi[subscript S] of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is more efficient than Kim and Warde stratified randomized response model and Mangat model.

This paper addresses the problem of estimating the population mean Y... of the study variable Y using information on population parameters associated with the auxiliary attribute Φ. We have suggested a class of estimators of population Y... mean using auxiliary attribute. Expressions of bias and mean squared error of the suggested class of estimators have been derived up to first order of approximation. Optimum condition is obtained at which the proposed class of estimators has minimum mean squared error. Theoretical findings are also supported through the aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

Abstract This article addresses the problem of estimating the population mean of variable y using information from multiple auxiliary variables in adaptive cluster sampling. We define a class of estimators based on p(>1) auxiliary variables x 1 , x 2 , … . . , x p , derive bias and mean squared error expressions up to order n−1, and determine optimal conditions for minimizing mean squared error. Our findings demonstrate that the proposed estimators outperform the multiple linear regression estimator. We also identify several existing estimators as members of this class, including those introduced by Dryver and Chao (2007), Chutiman and Kumphon (2008), Chutiman (2013), Chaudhry (2014), Chaudhry and Hanif (2015), and Yadav et al. (2016). Furthermore, we obtain the correct values of constants “a” and “b” for the Chaudhry and Hanif (2015) estimator, leading to an accurate expression for its minimum mean squared error. To assess the performance of these estimators, we conduct a simulation study. [ABSTRACT FROM AUTHOR]

In this article, first we obtained the correct mean square error expression of Gupta and Shabbir's linear weighted estimator of the ratio of two population proportions. Later we suggested the general class of ratio estimators of two population proportions. The usual ratio estimator, Wynn-type estimator, Singh, Singh, and Kaur difference-type estimator, and Gupta and Shabbir estimator have been found to be members of the suggested class.

In this article, we have suggested a new modified mixed randomized response (RR) model and studied its properties. It is shown that the proposed mixed RR model is always more efficient than the Kim and Warde's mixed RR model. The proposed mixed RR model has also been extended to stratified sampling. Numerical illustrations and graphical representations are also given in support of this study.

This article proposes two – stage stratified partial randomized response models based on Tracy and Osahan (1995) model and have large gain in precision. It is also shown that the proposed models are more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in the case of completely truthful reporting and less than completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper we have suggested a class of estimators of population mean of sensitive variable under optional randomized response technique as reported in Gupta et al (2014). We have obtained the mean squared error (MSE) of the suggested class of estimators up to the first order of approximation. The optimum conditions are obtained at which the (MSE) of the proposed class of estimators is minimum. An empirical study is carried out to show the performance of the suggested class of estimators over existing estimators .It is found that the performance of proposed class of estimators is better than the existing estimators including Grover and Kaur (2019) [ABSTRACT FROM AUTHOR]