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Stevia rebaudiana Bertoni is a perennial herb, widely used as a natural sweetener around the globe. The key compounds responsible for its sweetness includes stevioside and rebaudioside-A. In order to improve these steviol glycosides, the present study was initiated to study the effect of induced mutagenesis on growth parameters, steviol glycosides and nuclear DNA content in Stevia rebaudiana Bertoni using ten doses of gamma-rays (5-100 kR). Healthy seeds of 'Madhuguna' variety of Stevia rebaudiana Bertoni developed and maintained at stevia breeding farm, Agrotechnology division, CSIR-Institute of Himalayan Bioresource Technology, Palampur (HP), India were irradiated with ten doses of gamma rays (600 seeds each/dose) ranging from 5 kR to 100 kR (i.e., 5, 10, 15, 20, 30, 40, 50, 60, 80 and 100 kR) using Co60 gamma irradiation chamber at CCS Haryana Agricultural University, Hisar, (Haryana), India. Significant variations were recorded for all the seedling traits studied while major impact was noticed on the seedling after reaching the cotyledonary stage and doses above 40 kR showed absolute mortality of the seedlings. Based on probit analysis, the optimum LD50 dose lies in the range of 20-23 kR. Glycosidic profiling of 296 mutants using high-performance liquid chromatography showed decreased total steviol glycoside content with increased radiation dose. Doses 5 kR and 10 kR, were found to be effective in increasing the overall glycosidic content. A total of 72 promising mutants were also screened for increased rebaudioside-A stevioside ratio. Comparison of nuclear DNA content using flow cytometry revealed a similar decrease in the total nuclear DNA content with increase in dosage of gamma rays. The average genome size at 5, 10, 15, 20 and 30 kR treatments were 2.72, 2.69, 2.68, 2.70 and 2.66 pg as compared to 2.72 pg in control. Mild dose of gamma rays (5 and 10 kR) in stevia were found to be effective in improving the mean steviol glycoside content and may be used in future stevia mutation programmes. [ABSTRACT FROM AUTHOR]

AbstractVery little attention has been paid by the authors for providing improved imputation methods under correlated measurement errors (CMEs) using various sampling designs. This article addresses several power ratio imputation methods and the resulting estimators under ranked set sampling (RSS) when CMEs are present. The mean square error (MSE) is developed to assess how well the suggested estimators work under CMEs. The effectiveness of the proposed imputation methods and corresponding resultant estimators is assessed by a comprehensive simulation utilizing an artificially generated population. Further, an application of the proposed imputation methods is also provided using real data from Sweden’s municipalities consisting of the total number of seats in 1982 in municipal council taken as study variable and the number of conservative seats in 1982 in municipal council taken as auxiliary variable. The CMEs might arise due to common surveyor biases if the same surveyor collects information on both the “total number of seats in municipal council” and “the number of conservative seats in that municipal council”. The simulation and real data results show that the proposed power ratio imputation methods outperform the mean, conventional ratio, and product imputation methods. [ABSTRACT FROM AUTHOR]

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others. [ABSTRACT FROM AUTHOR]

Abstract.This article suggested some sampling strategies allied with Searls (1964) for mean estimation on current occasions under an optimal random non response framework using auxiliary information. The characteristics of each class or estimator under their respective strategy have been studied under the optimum replacement policy. We have gauged losses, which are carried out to show the dominance over complete response classes like Singh and Vishwakarma (2007, 2009). Appropriate recommendations have been made to the survey researchers for their real-life practical applications. [ABSTRACT FROM AUTHOR]

Missing data is an obligatory phenomenon in most of the sample surveys and when it is not handled cautiously at beginning of the study, outcomes may result to substantial biases in the survey estimates. Retaining such evidences into cognizance, this paper proffers few possible improved imputation methods to figure out the problem of missing data at the starting of the study and proffers computation procedure of the population mean using simple random sampling. The characteristics of the proffered imputation methods are ascertained till first order approximation pursued by a computational study carried out using several existent and hypothetical data sets. The findings are turned out rather promising showing dominance over the existing imputation methods. Further, apposite advices have been forwarded to the survey professionals for practical applications. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we intend to utilize the multi auxiliary information available under RSS for the imputation of missing data. The mean imputation, regression imputation methods, and power transformation imputation method are identified as special cases of the proposed imputation methods. These methods are dominated by the proposed imputation methods. The theoretical comparison provides the dominance conditions of the proposed imputation methods over their conventional counterparts. In support of the theoretical findings, a simulation study is considered over a hypothetically generated population. Furthermore, some real data examples are also provided to generalize the simulation results. [ABSTRACT FROM AUTHOR]

In survey sampling, several estimation procedures have been proffered by various prominent authors to compute the impact of measurement errors (ME) but the impact of correlated measurement errors (CME) has been computed only by Shalabh and Tsai [Ratio and product methods of estimation of population mean in the presence of correlated measurement errors. Commun Stat Simul Comput. 2016;46(7):5566–5593]. This study provides a novel approach to compute the impact of CME through some logarithmic-type estimators using simple random sampling (SRS). The properties of the proffered estimators have been studied and compared with the properties of the conventional estimators. A numerical study and a broad spectrum simulation study are accomplished over real and artificially generated populations to support the theoretical results. [ABSTRACT FROM AUTHOR]

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y ¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This paper proposes some optimal classes of estimators under ranked set sampling. We have acquainted some modifications of the difference and ratio type estimators under ranked set sampling, considered by various authors like Hansen et al., Srivastava, and Walsh in simple random sampling. We have demonstrated that the efficiency of the proposed optimal classes of estimators are always better than the other existing estimators. The theoretical results have been supported by a simulation study carried out over an artificially generated population. [ABSTRACT FROM AUTHOR]

In this article, we propose additive shared gamma frailty model and additive shared inverse Gaussian frailty model with Lindley as baseline distribution to analyze the infectious disease data set of McGilchrist and Aisbett. The Bayesian approach of Markov Chain Monte Carlo technique was employed to estimate the parameters involved in the models. A simulation study was also carried out to compare the true values and estimated values of the parameters. Comparison of proposed models and the existing models was also done by using Bayesian information criteria such as BIC, WBIC and Bayes factor. A better model was also recommended for infectious disease data. [ABSTRACT FROM AUTHOR]

This paper introduces some new ratio- and difference-type estimators of the population variance in the presence of random non-response based on Searls (The utilization of a known coefficient of variation in the estimation procedure. J Am Stat Asso. 1964;59:1225–1226) philosophy. The proposed ratio- and difference-type estimators remain better than the estimators obtained by Ahmeda et al. (Estimation of finite population variance in presence of random non-response using auxiliary variables. Infr Mang Sci. 2005;16(2):73–82) in the presence of random non-response using auxiliary variables. The properties (bias and mean square error) of the proposed estimators presented were derived up to the first-order approximation using the Taylor series approach. Conditions for which the new estimators more efficient than other estimators considered in the study were also established. Numerical examples were conducted, and the results revealed that the proposed class of estimators is more efficient than existing estimators. [ABSTRACT FROM AUTHOR]