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Douglas A. Wolfe - One of the best experts on this subject based on the ideXlab platform.

  • ranked set sampling for a Population Proportion allocation of sample units to each judgment order statistic
    Pakistan Journal of Statistics and Operation Research, 2012
    Co-Authors: Jessica Kohlschmidt, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling is an alternative to simple random sampling that has been shown to outperform simple random sampling in many situations by reducing the variance of an estimator, thereby providing the same accuracy with a smaller sample size than is needed in simple random sampling. Ranked set sampling involves preliminary ranking of potential sample units on the variable of interest using judgment or an auxiliary variable to aid in sample selection. Ranked set sampling prescribes the number of units from each rank order to be measured. Balanced ranked set sampling assigns equal numbers of sample units to each rank order. Unbalanced ranked set sampling allows unequal allocation to the various ranks, but this allocation may be sensitive to the quality of information available to do the allocation.  In this paper we use a simulation study to conduct a sensitivity analysis of optimal allocation of sample units to each of the order statistics in unbalanced ranked set sampling. Our motivating example comes from the National Survey of Families and Households.

  • Unbalanced Ranked Set Sampling for Estimating A Population Proportion Under Imperfect Rankings
    Communications in Statistics - Theory and Methods, 2009
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe, Steven N. Maceachern
    Abstract:

    The application of unbalanced ranked set sampling (RSS) to estimation of a Population Proportion has been studied for the perfect ranking situation. When the rankings are not perfect, the probabilities of success ranks for the judgment order statistics incorporate information on ranking errors as well as ranks. The objective of this article is to investigate the ranking errors effect of imperfection in rankings on unbalanced RSS for binary variables and provide methods to obtain estimates for the probabilities of success for the judgment order statistics using training samples so that Neyman allocation can be implemented. We also use a substantial data set, the NHANES III data, to demonstrate the feasibility and benefits of Neyman allocation in RSS for binary variables in the case of imperfect rankings.

  • An empirical assessment of ranking accuracy in ranked set sampling
    Computational Statistics & Data Analysis, 2006
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) involves ranking of potential sampling units on the variable of interest using judgment or an auxiliary variable to aid in sample selection. Its effectiveness depends on the success in this ranking. We provide an empirical assessment of RSS ranking accuracy in estimation of a Population Proportion.

  • Ranked set sampling for efficient estimation of a Population Proportion.
    Statistics in medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion. Copyright © 2005 John Wiley & Sons, Ltd.

  • ranked set sampling for efficient estimation of a Population Proportion
    Statistics in Medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion.

Haiying Chen - One of the best experts on this subject based on the ideXlab platform.

  • Unbalanced Ranked Set Sampling for Estimating A Population Proportion Under Imperfect Rankings
    Communications in Statistics - Theory and Methods, 2009
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe, Steven N. Maceachern
    Abstract:

    The application of unbalanced ranked set sampling (RSS) to estimation of a Population Proportion has been studied for the perfect ranking situation. When the rankings are not perfect, the probabilities of success ranks for the judgment order statistics incorporate information on ranking errors as well as ranks. The objective of this article is to investigate the ranking errors effect of imperfection in rankings on unbalanced RSS for binary variables and provide methods to obtain estimates for the probabilities of success for the judgment order statistics using training samples so that Neyman allocation can be implemented. We also use a substantial data set, the NHANES III data, to demonstrate the feasibility and benefits of Neyman allocation in RSS for binary variables in the case of imperfect rankings.

  • An empirical assessment of ranking accuracy in ranked set sampling
    Computational Statistics & Data Analysis, 2006
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) involves ranking of potential sampling units on the variable of interest using judgment or an auxiliary variable to aid in sample selection. Its effectiveness depends on the success in this ranking. We provide an empirical assessment of RSS ranking accuracy in estimation of a Population Proportion.

  • ranked set sampling for efficient estimation of a Population Proportion
    Statistics in Medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion.

  • Ranked set sampling for efficient estimation of a Population Proportion.
    Statistics in medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion. Copyright © 2005 John Wiley & Sons, Ltd.

  • Unbalanced ranked set sampling for estimating a Population Proportion.
    Biometrics, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    The application of ranked set sampling (RSS) techniques to data from a dichotomous Population is currently an active research topic, and it has been shown that balanced RSS leads to improvement in precision over simple random sampling (SRS) for estimation of a Population Proportion. Balanced RSS, however, is not in general optimal in terms of variance reduction for this setting. The objective of this article is to investigate the application of unbalanced RSS in estimation of a Population Proportion under perfect ranking, where the probabilities of success for the order statistics are functions of the underlying Population Proportion. In particular, the Neyman allocation, which assigns sample units for each order statistic Proportionally to its standard deviation, is shown to be optimal in the sense that it leads to minimum variance within the class of RSS estimators that are simple averages of the means of the order statistics. We also use a substantial data set, the National Health and Nutrition Examination Survey III (NHANES III) data, to demonstrate the feasibility and benefits of Neyman allocation in RSS for binary variables.

Elizabeth A. Stasny - One of the best experts on this subject based on the ideXlab platform.

  • ranked set sampling for a Population Proportion allocation of sample units to each judgment order statistic
    Pakistan Journal of Statistics and Operation Research, 2012
    Co-Authors: Jessica Kohlschmidt, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling is an alternative to simple random sampling that has been shown to outperform simple random sampling in many situations by reducing the variance of an estimator, thereby providing the same accuracy with a smaller sample size than is needed in simple random sampling. Ranked set sampling involves preliminary ranking of potential sample units on the variable of interest using judgment or an auxiliary variable to aid in sample selection. Ranked set sampling prescribes the number of units from each rank order to be measured. Balanced ranked set sampling assigns equal numbers of sample units to each rank order. Unbalanced ranked set sampling allows unequal allocation to the various ranks, but this allocation may be sensitive to the quality of information available to do the allocation.  In this paper we use a simulation study to conduct a sensitivity analysis of optimal allocation of sample units to each of the order statistics in unbalanced ranked set sampling. Our motivating example comes from the National Survey of Families and Households.

  • Unbalanced Ranked Set Sampling for Estimating A Population Proportion Under Imperfect Rankings
    Communications in Statistics - Theory and Methods, 2009
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe, Steven N. Maceachern
    Abstract:

    The application of unbalanced ranked set sampling (RSS) to estimation of a Population Proportion has been studied for the perfect ranking situation. When the rankings are not perfect, the probabilities of success ranks for the judgment order statistics incorporate information on ranking errors as well as ranks. The objective of this article is to investigate the ranking errors effect of imperfection in rankings on unbalanced RSS for binary variables and provide methods to obtain estimates for the probabilities of success for the judgment order statistics using training samples so that Neyman allocation can be implemented. We also use a substantial data set, the NHANES III data, to demonstrate the feasibility and benefits of Neyman allocation in RSS for binary variables in the case of imperfect rankings.

  • An empirical assessment of ranking accuracy in ranked set sampling
    Computational Statistics & Data Analysis, 2006
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) involves ranking of potential sampling units on the variable of interest using judgment or an auxiliary variable to aid in sample selection. Its effectiveness depends on the success in this ranking. We provide an empirical assessment of RSS ranking accuracy in estimation of a Population Proportion.

  • Ranked set sampling for efficient estimation of a Population Proportion.
    Statistics in medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion. Copyright © 2005 John Wiley & Sons, Ltd.

  • ranked set sampling for efficient estimation of a Population Proportion
    Statistics in Medicine, 2005
    Co-Authors: Haiying Chen, Elizabeth A. Stasny, Douglas A. Wolfe
    Abstract:

    Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a Population Proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a Population Proportion.

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

Ehsan Zamanzade - One of the best experts on this subject based on the ideXlab platform.