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minimum covariance estimator implemented in the libra toolbox  (MathWorks Inc)


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    MathWorks Inc minimum covariance estimator implemented in the libra toolbox
    Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available <t>LIBRA</t> <t>toolbox</t> <t>(Verboven</t> and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.
    Minimum Covariance Estimator Implemented In The Libra Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/minimum+covariance+estimator+implemented+in+the+libra+toolbox/pmc03397314-37-11-18?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    minimum covariance estimator implemented in the libra toolbox - by Bioz Stars, 2026-07
    90/100 stars

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    1) Product Images from "Better Ways to Improve Standards in Brain-Behavior Correlation Analysis"

    Article Title: Better Ways to Improve Standards in Brain-Behavior Correlation Analysis

    Journal: Frontiers in Human Neuroscience

    doi: 10.3389/fnhum.2012.00200

    Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available LIBRA toolbox (Verboven and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.
    Figure Legend Snippet: Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available LIBRA toolbox (Verboven and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.

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    minimum covariance estimator implemented in the libra toolbox  (MathWorks Inc)
    90
    MathWorks Inc minimum covariance estimator implemented in the libra toolbox
    Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available <t>LIBRA</t> <t>toolbox</t> <t>(Verboven</t> and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.
    Minimum Covariance Estimator Implemented In The Libra Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/minimum+covariance+estimator+implemented+in+the+libra+toolbox/pmc03397314-37-11-18?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    minimum covariance estimator implemented in the libra toolbox - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

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    Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available LIBRA toolbox (Verboven and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.

    Journal: Frontiers in Human Neuroscience

    Article Title: Better Ways to Improve Standards in Brain-Behavior Correlation Analysis

    doi: 10.3389/fnhum.2012.00200

    Figure Lengend Snippet: Statistical power (A) and false positive rates (B) for four statistical tests and four sample sizes based on 10,000 simulations (see for details) . Outliers can drastically inflate false positives for Pearson correlation (note the difference in scale for this test). Skipped correlation (Wilcox, ) is generally very susceptible to false positives under all conditions. Only Shepherd’s pi provides adequate statistical power and protection against false positives. The black line in (B) denotes the nominal false positive rate of 0.05. (C) Replot of data shown in Rousselet and Pernet’s Figure 2. The contour lines indicate the bootstrapped Mahalanobis distance D s from the bivariate mean in steps of six squared units (purple colors denote greater distances). Filled circles denote data included in the correlation, open circles denote outliers (see for details). The solid line is a linear regression over the data after outlier removal. The correlation statistics shown are Spearman’s rho , skipped correlation r ′ (critical t in parentheses), and Shepherd’s pi . Asterisks indicate significant results. All p -statistics rounded to third decimal. The freely available LIBRA toolbox (Verboven and Hubert, ) was used to calculate the skipped correlation. While the exact estimates of the t -statistic differ between R and MATLAB the conclusions about significance for these tests are very similar.

    Article Snippet: The skipped correlation employs a minimum covariance estimator implemented in the LIBRA toolbox (Verboven and Hubert, ) for MATLAB ( http://wis.kuleuven.be/stat/robust/LIBRA.html ).

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