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In this study, we introduce a novel combined ratio type estimator within the framework of Stratified Ranked Set Sampling to estimate the population mean of the study variable by incorporating bivariate auxiliary information. We conduct a comprehensive comparative analysis, including traditional combined ratio, combined regression, Shabbir and Khan [13], and Bhushan and Kumar [37] estimators. We assess the bias and mean squared error of the proposed estimator under the initial degree of approximation. The data source consists of COVID-19 data up to July 2023. Through empirical investigation and simulation studies, our proposed estimator consistently demonstrates superior performance compared to its counterparts, exhibiting the highest relative efficiency. These findings underscore the practical significance of our research in public health and decision-making, emphasizing the potential of this estimator to provide more accurate and reliable estimates in various applications involving ranked set sampling and auxiliary information. [ABSTRACT FROM AUTHOR]

The quality of soil maps can best be estimated by collecting additional data at locations selected by probability sampling. These data can be used in design-based estimation of map quality measures such as the population mean of the squared prediction errors (MSE) for continuous soil maps and overall accuracy for categorical soil maps. In areas with large differences in access costs it can be attractive to account for these differences in selecting validation locations. In this paper two types of sampling design are compared that take access costs into account: sampling with probabilities proportional to size (pps) and stratified simple random sampling (STSI). In pps the inverse of the square root of the access costs is used as a size variable. Two estimators of MSE are applied, the Hansen-Hurwitz and Hajek estimator. In STSI optimal strata are constructed based on access costs. Simple random sampling (SI) is taken as a reference design. The sampling strategies were compared on the basis of: 1) the variance of the estimated MSE; 2) the variance of the total pointwise access costs; 3) the 95-percentile of the sampling distribution of the total access costs. The comparison was done at equal expected total pointwise access costs. The sampling strategies were compared in a simulation study and a real-world case study in Anhui, China. In the case study car travel and hiking costs were considered in computing access costs per point. The results showed that the variance of estimated MSE with pps(Hansen-Hurwitz) was larger than with pps(Hajek) and STSI. The variances of estimated MSE of pps(Hajek) and STSI were about equal and smaller than that of SI. The gain in precision compared to SI depends on the cost distribution. The larger the coefficient of variation of the costs, the larger the gain. The 95 percentile of the sampling distribution of the total pointwise access costs with STSI was smaller than with pps and SI. The gain in precision of pps(Hajek) and STSI was about 30% accounting for hiking costs only, and about 10% accounting for the sum of car travel and hiking costs in the case study. The proposed sampling strategies are of interest for surveying any soil property in areas with marked differences in access costs, not just for validation of soil maps. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an improved estimator of finite population mean using information on two auxiliary attributes under simple random sampling (SRS) and two-phase sampling schemes. The bias and mean squared error (MSE) expressions of the proposed estimators are obtained up to first order of approximation. It is shown theoretically, that the proposed estimator is more efficient than the ratio-product type estimator, regression estimator, difference type estimator and Malik and Singh (2013) [13] estimator in both SRS and two phase sampling. An empirical study using two data sets is also conducted to support the theoretical findings. [Copyright &y& Elsevier]

Abstract: In this paper we propose a class of chain ratio-type exponential estimators for population mean in double sampling using two auxiliary variates. The properties of the proposed class of estimators are studied under large sample approximation. It has been shown that the proposed class of estimators is more efficient than the ratio estimator in double sampling , Chand’s (1975) chain-type ratio estimator and Singh and Vishwakarma (2007) exponential ratio estimator. An empirical study is carried out in support of the present study. [Copyright &y& Elsevier]

Abstract: We propose a two parameter ratio-product-ratio estimator for estimating a finite population variance based on simple random sampling without replacement. The bias and mean square error of the proposed estimator are obtained to the first degree of approximation. We have derived the conditions for the parameters under which the proposed estimator has smaller mean square error than the sample variance, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using different data sets. [Copyright &y& Elsevier]

Abstract: This paper proposes two product type exponential estimators of population mean of study variable using linear transformation of auxiliary variable with the assumption that the extreme values (i.e. maximum and minimum values) of auxiliary variable in the population are known in advance. The expressions for biases and mean square errors of proposed estimators are obtained, up to first order of approximation. Theoretical conditions have been made under which our proposed estimators are superior to the existing ones. The overall preference regions of all the estimators considered in this paper are constructed and represented them diagrammatically. A numerical illustration is considered to support and verify the superiority of our proposed estimators. [Copyright &y& Elsevier]

Abstract: In this paper, we derive the generalized maximum likelihood estimator (GMLE) of the survival function of a random variable for censored data under the Koziol–Green model. Its small sample properties are compared with those of Abdushukurov–Cheng–Lin (ACL), Kaplan–Meier, and Nelson–Aalen type estimators. The large sample analysis indicates that the GMLE, ACL, and Nelson–Aalen type estimators are asymptotically equivalent. [Copyright &y& Elsevier]

Abstract: In this paper, we suggest the maximum likelihood estimator (MLE) of the quadratic function p 2 + βp (for different values of β) of the Bernoulli parameter, that is discussed for the inverse sampling (IS) and fixed size sampling (FSS) methods. Moreover, the IS method is compared with FSS method based on mean squared error (MSE). In consequence of MSE for MLE of the quadratic function p 2 + βp in two sampling methods are complicated, so we also give a computational comparison of MSE for p 2 + βp to assess the performance of two sampling methods by using numerical method. [Copyright &y& Elsevier]

The mean of the usual balanced ranked set sample (BRSS), as introduced by McIntyre [Aust. J. Agric. Res. 3 (1952) 385], is more efficient as an estimator of the population mean than the mean of a simple random sample (SRS). Hence in this regard, BRSS is considered as a non-parametric procedure. However, for estimating other parameters of the population, the BRSS is not as universal (non-parametric) as in the case of the mean of the population. In this paper, it is shown that, under some suitable conditions, any reasonable estimator of any parameter of a population based on a SRS, can be always dominated by the corresponding estimator using some RSS plans. [Copyright &y& Elsevier]