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The paper deals with the problem of estimating the general parameter P(α) { = (Ȳ0/Ȳ1α), Ȳ1 ≠ 0,α being a scalar which takes values -1, 0 and 1; and Ȳj is the population mean of the study variate yj, j = 0,1 } using double sampling for stratification (DSS) based on auxiliary information (i) when there is total response on the auxiliary variable x used in estimating the stratum weight Wh { = (Nh/N), Nh and N being the size of the hth stratum and the population size N respectively} and on the study variates (y0, y1) ; and (ii) when there is total response on the auxiliary variable x and incomplete response on the study variables (y0, y1), see Okafor (1996). Classes of estimators for the general parameter P(α) in both the situations have been proposed and their properties are studied. Asymptotic optimum estimators (AOEs) in the classes are investigated alongwith their approximate variance formulae. The present study also generalizes the work of Okafor (1996) and Singh and Vishwakarma (2007). [ABSTRACT FROM AUTHOR]

For estimating the shape parameter and measure of dispersion of the Inverse Gaussian distribution, a general class of estimators using prior information has been defined. The resulting estimators have been compared with minimum mean squared error estimator, uniformly minimum variance unbiased estimator, Pandey and Malik (1988) estimator. Realistic conditions have been obtained under which the suggested estimators are better than the well-known minimum mean squared error estimator and uniformly minimum variance unbiased estimator. Numerical illustrations are given in the support of the proposed study. [ABSTRACT FROM AUTHOR]

A general procedure is considered for estimating the population mean of a study variate using double sampling for stratification based on auxiliary information. Expressions for bias and variance of the proposed classes of estimators up to the first degree of approximation are derived. An asymptotically optimum estimator in the classes is identified with their approximate variance formulae. The proposed classes of estimators are compared with the corresponding class of estimators based on unstratified double sampling. [ABSTRACT FROM AUTHOR]

Recent developments in statistical quality control include the study of the process capability. Much attention has been given to the estimation and the test of hypotheses in this regard. Due to considerable handling of parameter(s) in the past, one may have some prior information in the form of either a point, an interval or in the form of prior distribution. This article provides a class of shrinkage estimators for Cp when apriori or guessed value, say σ0 of standard deviation σ is available. Subsequently, the Bayesian estimation of Cp has been done under squared error loss function by assuming quasi-prior distributions of σ and (1/σ). Bayesian estimation of Cp, around (1/σ0) is further tried. The investigation is supported by numerical illustrations wherever necessary. [ABSTRACT FROM AUTHOR]

Recent developments in statistical quality control include the study of the process capability. Much attention has been given to the estimation and the test of hypotheses in this regard. Due to considerable handling of parameter(s) in the past, one may have some prior information in the form of either a point, an interval or in the form of prior distribution. This article provides a class of shrinkage estimators for Cp when apriori or guessed value, say σ0 of standard deviation σ is available. Subsequently, the Bayesian estimation of Cp has been done under squared error loss function by assuming quasi-prior distributions of σ and (1/σ). Bayesian estimation of Cp, around (1/σ0) is further tried. The investigation is supported by numerical illustrations wherever necessary. [ABSTRACT FROM AUTHOR]

It is well accepted that suitable additional information improves the efficiency of an estimator but at the same time, it faced the situation of nonresponse. The two-occasion successive sampling is useful to handle the absence of a full response from respondents. In this present study, we have developed exponential estimators for population mean using a subsampling nonrespondent procedure. To show the efficacy of the recommended estimators, several properties are derived, and respective optimum replacement strategies are inferred. To evaluate the performance of the recommended estimators empirically, a computational study is carried out as well. It was found that the recommended estimators outperform the existing ones under the nonresponse in successive sampling. [ABSTRACT FROM AUTHOR]

In this paper we propose alternative estimators to ratio estimators given by Kadilar and Cingi (2006) and a class of estimators for population variance using the same approach adopted by Srivenkataramana (1980). Conditions under which the proposed estimators are more efficient than usual unbiased estimator, Isaki (1983) estimator, Biradar and Singh (1994) estimator and the estimators given by Kadilar and Cingi (2006) are obtained. The exact expressions for biases and mean squared errors of the proposed estimators are obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators. Also following Searls (1964), generalized version of the proposed class of estimators is also studied with its properties. [ABSTRACT FROM AUTHOR]

The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators., (© 2024. The Author(s).)

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., (© 2023. The Author(s).)