The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform

Hulya Cingi - One of the best experts on this subject based on the ideXlab platform.

  • a new class of exponential regression cum Ratio Estimator in two phase sampling
    Quality Engineering, 2015
    Co-Authors: Nilgun Ozgul, Hulya Cingi
    Abstract:

    In this paper, we propose a new class of exponential regression cum Ratio Estimator using the auxiliary variable for the estimation of the finite population mean under two phase sampling scheme. The Bias and Mean Square Error (MSE) equations of the proposed Estimator are obtained and compared with the MSE equations of some existing Estimators in two phase sampling. We find theoretically the proposed Estimator is always more efficient than classical Ratio and regression Estimators, Singh and Vishwakarma [17] Ratio type exponential Estimator in two phase sampling. In addition, theoric results are supported by a numerical example using original data sets.

  • Ratio Estimator for the population mean using ranked set sampling
    Statistical Papers, 2007
    Co-Authors: Cem Kadilar, Yesim Unyazici, Hulya Cingi
    Abstract:

    We adapt the Ratio estimation using ranked set sampling, suggested by Samawi and Muttlak (Biometr J 38:753–764, 1996), to the Ratio Estimator for the population mean, based on Prasad (Commun Stat Theory Methods 18:379–392, 1989), in simple random sampling. Theoretically, we show that the proposed Ratio Estimator for the population mean is more efficient than the Ratio Estimator, in Prasad (1989), in all conditions. In addition, we support this theoretical result with the aid of a numerical example.

  • A New Ratio Estimator in Stratified Random Sampling
    Communications in Statistics - Theory and Methods, 2005
    Co-Authors: Cem Kadilar, Hulya Cingi
    Abstract:

    In this article, we suggest a new Ratio Estimator in stratified random sampling based on the Prasad (1989) Estimator. Theoretically, we obtain the mean square error (MSE) for this Estimator and compare it with the MSE of traditional combined Ratio estimate. By this comparison, we demonstrate that proposed Estimator is more efficient than combined Ratio estimate in all conditions. In addition, this theoretical result is supported by a numerical example.

  • Sampling Theory A New Ratio Estimator in Stratified Random Sampling
    2005
    Co-Authors: Cem Kadilar, Hulya Cingi
    Abstract:

    In this article, we suggest a new Ratio Estimator in stratified random sampling based on the Prasad (1989) Estimator. Theoretically, we obtain the mean square error (MSE) for this Estimator and compare it with the MSE of traditional combined Ratio estimate. By this comparison, we demonstrate that proposed Estimator is more efficient than combined Ratio estimate in all conditions. In addition, this theoretical result is supported by a numerical example.

Henry M. Murray - One of the best experts on this subject based on the ideXlab platform.

  • Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling
    Open Journal of Statistics, 2012
    Co-Authors: Oscar Ngesa, George Orwa, Romanus Odhiambo Otieno, Henry M. Murray
    Abstract:

    Olkin [1] proposed a Ratio Estimator considering p auxiliary variables under simple random sampling. As is expected, Simple Random Sampling comes with relatively low levels of precision especially with regard to the fact that its variance is greatest amongst all the sampling schemes. We extend this to stratified random sampling and we consider a case where the strata have varying weights. We have proposed a Multivariate Ratio Estimator for the population mean in the presence of two auxiliary variables under Stratified Random Sampling with L strata. Based on an empirical study with simulations in R statistical software, the proposed Estimator was found to have a smaller bias as compared to Olkin’s Estimator.

Christopher Ouma - One of the best experts on this subject based on the ideXlab platform.

  • Mixture Regression-Cum-Ratio Estimator Using Multi-Auxiliary Variables and Attributes in Single-Phase Sampling
    Open Journal of Statistics, 2014
    Co-Authors: Teresio Mutembei, John Kung’u, Christopher Ouma
    Abstract:

    In this paper, we have proposed a class of mixture regression-cum-Ratio Estimator for estimating population mean by using information on multiple auxiliary variables and attributes simultaneously in single-phase sampling and analyzed the properties of the Estimator. An empirical was carried out to compare the performance of the proposed Estimator with the existing Estimators of finite population mean using simulated population. It was found that the mixture regression-cum-Ratio Estimator was more efficient than Ratio and regression Estimators using one auxiliary variable and attribute, Ratio and regression Estimators using multiple auxiliary variables and attributes and regression-cum-Ratio Estimators using multiple auxiliary variables and attributes in single-phase sampling for finite population.

Oscar Ngesa - One of the best experts on this subject based on the ideXlab platform.

  • Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling
    Open Journal of Statistics, 2012
    Co-Authors: Oscar Ngesa, George Orwa, Romanus Odhiambo Otieno, Henry M. Murray
    Abstract:

    Olkin [1] proposed a Ratio Estimator considering p auxiliary variables under simple random sampling. As is expected, Simple Random Sampling comes with relatively low levels of precision especially with regard to the fact that its variance is greatest amongst all the sampling schemes. We extend this to stratified random sampling and we consider a case where the strata have varying weights. We have proposed a Multivariate Ratio Estimator for the population mean in the presence of two auxiliary variables under Stratified Random Sampling with L strata. Based on an empirical study with simulations in R statistical software, the proposed Estimator was found to have a smaller bias as compared to Olkin’s Estimator.

Sat Gupta - One of the best experts on this subject based on the ideXlab platform.

  • estimation of population coefficient of variation in simple and stratified random sampling under two phase sampling scheme when using two auxiliary variables
    Communications in Statistics-theory and Methods, 2017
    Co-Authors: Javid Shabbir, Sat Gupta
    Abstract:

    We propose an improved class of exponential Ratio type Estimators for coefficient of variation (CV) of a finite population in simple and stratified random sampling using two auxiliary variables under two-phase sampling scheme. We examine the properties of the proposed Estimators based on first order of approximation. The proposed class of Estimators is more efficient than the usual sample coefficient of variation (CV) Estimator, Ratio Estimator, exponential Ratio Estimator, usual difference Estimator and Hamad et al. (2013) difference type Estimator. We also use real data sets for numerical comparisons.

  • estimation of the mean of a sensitive variable in the presence of auxiliary information
    Communications in Statistics-theory and Methods, 2012
    Co-Authors: Sat Gupta, Javid Shabbir, Rita Sousa, Pedro Cortereal
    Abstract:

    Sousa et al. (2010) introduced a Ratio Estimator for the mean of a sensitive variable and showed that this Estimator performs better than the ordinary mean Estimator based on a randomized response technique (RRT). In this article, we introduce a regression Estimator that performs better than the Ratio Estimator even for modest correlation between the primary and the auxiliary variables. The underlying assumption is that the primary variable is sensitive in nature but a non sensitive auxiliary variable exists that is positively correlated with the primary variable. Expressions for the Bias and MSE (Mean Square Error) are derived based on the first order of approximation. It is shown that the proposed regression Estimator performs better than the Ratio Estimator and the ordinary RRT mean Estimator (that does not utilize the auxiliary information). We also consider a generalized regression-cum-Ratio Estimator that has even smaller MSE. An extensive simulation study is presented to evaluate the performances o...

  • On the use of transformed auxiliary variables in estimating population mean by using two auxiliary variables
    Journal of Statistical Planning and Inference, 2007
    Co-Authors: Sat Gupta, Javid Shabbir
    Abstract:

    Abstract This paper focuses on the use of auxiliary variables in estimating the finite population mean. We introduce a new class of Estimators and compare its performance with several existing Estimators such as ordinary sample mean, conventional Ratio Estimator and Estimators by Sisodia and Dwivedi [1981. A modified Ratio Estimator using coefficient of variation of auxiliary variable. J. Indian Soc. Agricultural Statist. 33, 13–18], Singh and Kakran [1993. A modified Ratio Estimator using known coefficient of kurtosis of an auxiliary character. Unpublished manuscripts] and Upadhyaya and Singh [1999. Use of transformed auxiliary variable in estimating the finite population mean. Biometrical J. 41(5), 627–636]. The new Estimator performs better than the other Estimators considered here.

  • A New Estimator of Population Mean in Stratified Sampling
    Communications in Statistics - Theory and Methods, 2006
    Co-Authors: Javid Shabbir, Sat Gupta
    Abstract:

    Kadilar and Cingi (2005) have suggested a new Ratio Estimator in stratified sampling. The efficiency of this Estimator is compared with the traditional combined Ratio Estimator on the basis of mean square error (MSE). We propose another Estimator by utilizing a simple transformation introduced by Bedi (1996). The proposed Estimator is found to be more efficient than the traditional combined Ratio Estimator as well as the Kadilar and Cingi (2005) Ratio Estimator.