The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Jinyuan Liu - One of the best experts on this subject based on the ideXlab platform.
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Extending the Mann‐Whitney‐Wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
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extending the mann whitney wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in Medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
Tuo Lin - One of the best experts on this subject based on the ideXlab platform.
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Extending the Mann‐Whitney‐Wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
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extending the mann whitney wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in Medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
John M Lachin - One of the best experts on this subject based on the ideXlab platform.
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power and sample size evaluation for the cochran mantel haenszel mean score wilcoxon Rank Sum Test and the cochran armitage Test for trend
Statistics in Medicine, 2011Co-Authors: John M LachinAbstract:The power of a chi-square Test, and thus the required sample size, are a function of the noncentrality parameter that can be obtained as the limiting expectation of the Test statistic under an alternative hypothesis specification. Herein, we apply this principle to derive simple expressions for two Tests that are commonly applied to discrete ordinal data. The Wilcoxon Rank Sum Test for the equality of distributions in two groups is algebraically equivalent to the Mann–Whitney Test. The Kruskal–Wallis Test applies to multiple groups. These Tests are equivalent to a Cochran–Mantel–Haenszel mean score Test using Rank scores for a set of C-discrete categories. Although various authors have assessed the power function of the Wilcoxon and Mann–Whitney Tests, herein it is shown that the power of these Tests with discrete observations, that is, with tied Ranks, is readily provided by the power function of the corresponding Cochran–Mantel–Haenszel mean scores Test for two and R > 2 groups. These expressions yield results virtually identical to those derived previously for Rank scores and also apply to other score functions. The Cochran–Armitage Test for trend assesses whether there is an monotonically increasing or decreasing trend in the proportions with a positive outcome or response over the C-ordered categories of an ordinal independent variable, for example, dose. Herein, it is shown that the power of the Test is a function of the slope of the response probabilities over the ordinal scores assigned to the groups that yields simple expressions for the power of the Test. Copyright © 2011 John Wiley & Sons, Ltd.
Tian Chen - One of the best experts on this subject based on the ideXlab platform.
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Extending the Mann‐Whitney‐Wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
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extending the mann whitney wilcoxon Rank Sum Test to survey data for comparing mean Ranks
Statistics in Medicine, 2021Co-Authors: Tuo Lin, Tian Chen, Jinyuan LiuAbstract:Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) Rank Sum Test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW Test is called for when two-sample t-Tests (with or without equal variance asSumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW Test to survey data to Test the null of equal mean Rank. Although not as popular as the mean and median, the mean Rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.
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extending the mann whitney wilcoxon Rank Sum Test to longitudinal regression analysis
Journal of Applied Statistics, 2014Co-Authors: R. Chen, Tian Chen, Hui Zhang, Changyong FengAbstract:Outliers are commonly observed in psychosocial research, generally resulting in biased estimates when comparing group differences using popular mean-based models such as the analysis of variance model. Rank-based methods such as the popular Mann–Whitney–Wilcoxon (MWW) Rank Sum Test are more effective to address such outliers. However, available methods for inference are limited to cross-sectional data and cannot be applied to longitudinal studies under missing data. In this paper, we propose a generalized MWW Test for comparing multiple groups with covariates within a longitudinal data setting, by utilizing the functional response models. Inference is based on a class of U-statistics-based weighted generalized estimating equations, providing consistent and asymptotically normal estimates not only under complete but missing data as well. The proposed approach is illustrated with both real and simulated study data.
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Extending the Mann–Whitney–Wilcoxon Rank Sum Test to longitudinal regression analysis
Journal of Applied Statistics, 2014Co-Authors: R. Chen, Tian Chen, Hui Zhang, Changyong FengAbstract:Outliers are commonly observed in psychosocial research, generally resulting in biased estimates when comparing group differences using popular mean-based models such as the analysis of variance model. Rank-based methods such as the popular Mann–Whitney–Wilcoxon (MWW) Rank Sum Test are more effective to address such outliers. However, available methods for inference are limited to cross-sectional data and cannot be applied to longitudinal studies under missing data. In this paper, we propose a generalized MWW Test for comparing multiple groups with covariates within a longitudinal data setting, by utilizing the functional response models. Inference is based on a class of U-statistics-based weighted generalized estimating equations, providing consistent and asymptotically normal estimates not only under complete but missing data as well. The proposed approach is illustrated with both real and simulated study data.
Zhiyuan Luo - One of the best experts on this subject based on the ideXlab platform.
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gene selection using wilcoxon Rank Sum Test and support vector machine for cancer classification
Computational Intelligence and Security, 2007Co-Authors: Chen Liao, Zhiyuan LuoAbstract:Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon Rank Sum Test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon Rank Sum Test is used to select a subset. Then each selected gene is trained and Tested using SVM classifier with linear kernel separately, and genes with high Testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with the final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.
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CIS - Gene Selection Using Wilcoxon Rank Sum Test and Support Vector Machine for Cancer Classification
Computational Intelligence and Security, 2007Co-Authors: Chen Liao, Zhiyuan LuoAbstract:Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon Rank Sum Test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon Rank Sum Test is used to select a subset. Then each selected gene is trained and Tested using SVM classifier with linear kernel separately, and genes with high Testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with the final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.
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Gene Selection for Cancer Classification using Wilcoxon Rank Sum Test and Support Vector Machine
2006 International Conference on Computational Intelligence and Security, 2006Co-Authors: Chen Liao, Zhiyuan LuoAbstract:Gene selection is an important problem in microarray data processing. A new gene selection method based on Wilcoxon Rank Sum Test and Support Vector Machine (SVM) is proposed in this paper. First, Wilcoxon Rank Sum Test is used to select a subset. Then each selected gene is trained and Tested using SVM classifier with linear kernel separately, and genes with high Testing accuracy rates are chosen to form the final reduced gene subset. Leave-one-out cross validation (LOOCV) classification results on two datasets: Breast Cancer and ALL/AML leukemia, demonstrate the proposed method can get 100% success rate with final reduced subset. The selected genes are listed and their expression levels are sketched, which show that the selected genes can make clear separation between two classes.
