The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Cem Kadilar - One of the best experts on this subject based on the ideXlab platform.
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On estimating the population mean using auxiliary character in Stratified Random sampling
Journal of Statistics and Management Systems, 2020Co-Authors: Tolga Zaman, Cem KadilarAbstract:In this article, we propose an exponential estimator in the Stratified Random sampling taking an auxiliary attribute. Expressions for MSE of the proposed estimators are derived up to first order of...
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In-type estimators for the population variance in Stratified Random sampling
Communications in Statistics - Simulation and Computation, 2019Co-Authors: Hatice Oncel Cekim, Cem KadilarAbstract:In the Stratified Random sampling, the variance estimators are popularly proposed by using the ratio, product, regression, and exponential type estimators. Up to now, an alternative to these estima...
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New families of unbiased estimators in Stratified Random sampling
Journal of Statistics and Management Systems, 2018Co-Authors: Hatice Oncel Cekim, Cem KadilarAbstract:We improve two families of unbiased estimators in general form for estimating the finite population mean in Stratified Random sampling. These estimators are developed from the unbiased formal for t...
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Improved Dual to Ratio Cum Dual to Product Estimator in the Stratified Random Sampling
2015Co-Authors: Subhash Kumar Yadav, Cem Kadilar, S. S. Mishra, Alok Kumar ShuklaAbstract:In this article, we propose an improved dual to ratio cum dual to product estimator of the population mean under the Stratified Random sampling scheme. The expressions for the bias and mean squared error (MSE) of the proposed estimator are found by the first degree of approximation. The optimum value of the constant, which minimizes the MSE of the proposed estimator, is also obtained. Efficiency comparisons are performed between the proposed estimator and many estimators in Literature under the Stratified Random sampling and the efficiency conditions of the proposed estimator are determined. Finally, an empirical study is carried out which shows the performance of the proposed estimator along with the existing estimators under the Stratified Random sampling.
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A new kind estimator for the population mean in the Stratified Random sampling
2015Co-Authors: Gamze Ozel, Cem KadilarAbstract:In this paper, a new exponential type estimator has been developed in the Stratified Random sampling for the population mean. The optimum property of the suggested strategy has been studied. Comparisons of the efficiency of the proposed estimator under the optimal condition with other estimators have been presented through empirical investigations. It is shown that the proposed exponential type estimator is more efficient than ratio and product estimators.To judge the merits of the suggested class of estimators over others, a numerical example is carried out.
Rajesh Singh - One of the best experts on this subject based on the ideXlab platform.
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A new estimator for population mean using two auxiliary variables in Stratified Random sampling
Journal of Information and Optimization Sciences, 2017Co-Authors: Sachin Malik, Rajesh SinghAbstract:In this paper, we suggest an estimator using two auxiliary variables in Stratified Random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other consi...
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Exponential Ratio-Product Type Estimators Under Second Order Approximation In Stratified Random Sampling
viXra, 2016Co-Authors: Rajesh Singh, Prayas Sharma, Florentin SmarandacheAbstract:Singh et al. (20009) introduced a family of exponential ratio and product type estimators in Stratified Random sampling. Under Stratified Random sampling without replacement scheme, the expressions of bias and mean square error (MSE) of Singh et al. (2009) and some other estimators, up to the first- and second-order approximations are derived. Also, the theoretical findings are supported by a numerical example.
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An Improved Suggestion in Stratified Random Sampling Using Two Auxiliary Variables
viXra, 2016Co-Authors: Rajesh Singh, Sachin Malik, Florentin SmarandacheAbstract:In this paper, we suggest an estimator using two auxiliary variables in Stratified Random sampling following Malik and Singh. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data.
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Improved estimator of finite population mean using auxiliary attribute in Stratified Random sampling
arXiv: Statistics Theory, 2014Co-Authors: Hemant K. Verma, Prayas Sharma, Rajesh SinghAbstract:The present study discuss the problem of estimating the finite population mean using auxiliary attribute in Stratified Random sampling. In this paper taking the advantage of point bi-serial correlation between the study variable and auxiliary attribute, we have improved the estimation of population mean in Stratified Random sampling. The expressions for Bias and Mean square error have been derived under Stratified Random sampling. In addition, an empirical study has been carried out to examine the merits of the proposed estimator over the existing estimators.
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A New Estimator For Population Mean Using Two Auxiliary Variables in Stratified Random Sampling
arXiv: Applications, 2014Co-Authors: Rajesh Singh, Sachin MalikAbstract:In this paper, we suggest an estimator using two auxiliary variables in Stratified Random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data. Key words: Study variable, auxiliary variable, Stratified Random sampling, bias and mean squared error.
Rogelio Ramos-quiroga - One of the best experts on this subject based on the ideXlab platform.
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Optimum Allocation in Multivariate Stratified Random Sampling: A Modified Prékopa’s Approach
Journal of Mathematical Modelling and Algorithms in Operations Research, 2013Co-Authors: José A. Díaz-garcía, Rogelio Ramos-quirogaAbstract:Considering the possible correlation between the characteristics (variables) in multivariate Stratified Random sampling, a modified Prekopa’s approach is suggested for the problem of optimum allocation in multivariate Stratified Random sampling. An example is solved by applying the proposed methodology.
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Optimum allocation in multivariate Stratified Random sampling: stochastic matrix mathematical programming
Statistica Neerlandica, 2012Co-Authors: José A. Dí Az-garcí A, Rogelio Ramos-quirogaAbstract:The allocation problem for multivariate Stratified Random sampling as a problem of stochastic matrix integer mathematical programming is considered, minimizing the estimated covariance matrix of estimated means subject to fixed cost or fixed total sample size. With these aims the asymptotic normality of sample covariance matrices for each strata is established. Some alternative approaches are suggested for its solution. An example is solved by applying the proposed techniques.
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Optimum allocation in multivariate Stratified Random sampling: Stochastic matrix optimisation
arXiv: Statistics Theory, 2011Co-Authors: José A. Díaz-garcía, Rogelio Ramos-quirogaAbstract:The allocation problem for multivariate Stratified Random sampling as a problem of stochastic matrix integer mathematical programming is considered. With these aims the asymptotic normality of sample covariance matrices for each strata is established. Some alternative approaches are suggested for its solution. An example is solved by applying the proposed techniques.
Javid Shabbir - One of the best experts on this subject based on the ideXlab platform.
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A parent-generalized family of chain ratio exponential estimators in Stratified Random sampling using supplementary variables
Communications in Statistics - Simulation and Computation, 2020Co-Authors: Siraj Muneer, Alamgir Khalil, Javid ShabbirAbstract:In this article, we propose a parent-generalized family of chain exponential ratio type estimators in Stratified Random sampling to estimate the finite population mean using known information on tw...
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estimation of population mean in the presence of measurement error and non response under Stratified Random sampling
PLOS ONE, 2018Co-Authors: Erum Zahid, Javid ShabbirAbstract:In the present paper we propose an improved class of estimators in the presence of measurement error and non-response under Stratified Random sampling for estimating the finite population mean. The theoretical and numerical studies reveal that the proposed class of estimators performs better than other existing estimators.
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improved family of ratio estimators in simple and Stratified Random sampling
Communications in Statistics-theory and Methods, 2013Co-Authors: Abdul Haq, Javid ShabbirAbstract:This article proposes two improved family of estimators for estimating the finite population mean in simple Random sampling (SRS) and Stratified Random sampling (S t RS). The proposed estimators always perform better than a family of ratio estimators suggested by Khoshnevisan et al. (2007) in SRS and Koyuncu and Kadilar (2009a) in S t RS. They also perform better than the ratio estimator given by Gupta and Shabbir (2008) in SRS and Koyuncu and Kadilar (2010) and Shabbir and Gupta (2011) in S t RS. The expressions for bias and mean squared error (MSE) of considered estimators are obtained. The results are illustrated by real data sets.
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On the ratio method of estimation via auxiliary attributes in simple and Stratified Random sampling
2013Co-Authors: Zardad Khan, Javid Shabbir, Martin Griffiths, Berthold LausenAbstract:In this paper we have proposed a general class of ratio-cum-product type estimators that uses information on the auxiliary attributes (φ) available along with the study variable y in simple Random sampling. The proposed estimators are compared, both theoretically and empirically, using two data sets with some conventional estimators (like simple mean per unit estimator ȳ, Naik and Gupta [11] estimator, Singh et al. [16] estimators) and it is shown that the suggested estimators are always more efficient than the classical estimators. The proposed class of estimators is then extended to Stratified Random sampling for further improving its efficiency, among other reasons (see Cochran [3]). Theoretical and empirical comparisons are conducted using the same data sets. Information on population parameters of auxiliary attributes and some real constants denoting weights are utilized in a generalized way (both in simple and Stratified Random sampling). Finally, some suggestions are given for further research into our proposed classes of estimators. AMS subject classification:
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On Estimating Finite Population Mean in Simple and Stratified Random Sampling
Communications in Statistics - Theory and Methods, 2010Co-Authors: Javid Shabbir, Sat GuptaAbstract:In this article, we propose an exponential ratio type estimator for estimating the finite population mean in simple and Stratified Random sampling. The properties of the proposed estimator are obtained and comparison is made with some of the existing estimators. The proposed estimator is found to perform better than the usual mean, ratio, exponential ratio, traditional regression and Pandy (1980) estimators in simple and Stratified Random sampling. We use six data sets for simple Random sampling case and two data sets for Stratified Random sampling case to compare the performances of all of the estimators considered here.
Gajendra K. Vishwakarma - One of the best experts on this subject based on the ideXlab platform.
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Generalized Classes of Regression-Cum-Ratio Estimators of Population Mean in Stratified Random Sampling
Proceedings of the National Academy of Sciences India Section A: Physical Sciences, 2019Co-Authors: Manish Kumar, Gajendra K. VishwakarmaAbstract:In this paper, classes of separate and combined regression-cum-ratio estimators have been proposed for estimating the finite population mean in Stratified Random sampling. The expressions for biases and mean square errors (MSEs) of the proposed classes have been derived to the first order of approximation. It has also been verified that the proposed classes of estimators, at their optimum conditions, are equivalent to the separate regression estimator. The proposed classes of estimators have been compared with the other existing estimators using MSE criterion, and the conditions under which the proposed classes perform better have been obtained. Numerical illustrations are given in support of theoretical findings. Relevance of the work The estimation theory is relevant to various interdisciplinary areas of research including economics, clinical trials, population studies, engineering, agriculture, etc. Also, the problem of estimation of mean is of huge importance in research, for instance, the estimation of: average agricultural production, average life span of persons in a region, mean concentration of dissolved minerals in water, and much more. For the estimation of mean, several design-based approaches are being widely used, for instance, simple Random sampling, Stratified Random sampling, two-phase sampling, etc. If the population under study is homogeneous, then the simple Random sampling design is used at the estimation stage. However, in various practical situations, the research study is based on the heterogeneous population, and in that case the Stratified Random sampling procedure is preferable over the simple Random sampling. Considering the above fact, an attempt has been made in this paper to develop the classes of generalized estimators for the mean of the variable under study using Stratified Random sampling.
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some families of estimators of variance of Stratified Random sample mean using auxiliary information
Journal of statistical theory and practice, 2008Co-Authors: Housila P. Singh, Gajendra K. VishwakarmaAbstract:In this paper we have considered the problem of estimating the variance of the Stratified Random sample mean using information on a supplementary variate x. Various classes of estimators have been proposed and their properties are studied. It has been shown that the proposed classes of estimators are more efficient than usual unbiased estimator. An empirical study is carried out in support of the present study.
