The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Esteban Roulet - One of the best experts on this subject based on the ideXlab platform.
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Kolmogorov–Smirnov Test as a tool to study the distribution of ultra-high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
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Kolmogorov Smirnov Test as a tool to study the distribution of ultra high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
Diego Harari - One of the best experts on this subject based on the ideXlab platform.
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Kolmogorov–Smirnov Test as a tool to study the distribution of ultra-high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
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Kolmogorov Smirnov Test as a tool to study the distribution of ultra high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
Silvia Mollerach - One of the best experts on this subject based on the ideXlab platform.
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Kolmogorov–Smirnov Test as a tool to study the distribution of ultra-high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
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Kolmogorov Smirnov Test as a tool to study the distribution of ultra high energy cosmic ray sources
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: Diego Harari, Silvia Mollerach, Esteban RouletAbstract:We analyse in detail the two-dimensional Kolmogorov–Smirnov Test as a tool to learn about the distribution of the sources of the ultra-high energy cosmic rays. We confront, in particular, models based on active galactic nuclei observed in X-rays, galaxies observed in H i and isotropic distributions, discussing how this method can be used not only to reject isotropy but also to support or reject specific source models, extending results obtained recently in the literature.
Rand R. Wilcox - One of the best experts on this subject based on the ideXlab platform.
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Encyclopedia of Biostatistics - Kolmogorov–Smirnov Test
Encyclopedia of Biostatistics, 2005Co-Authors: Rand R. WilcoxAbstract:This is a distribution-free method for comparing two empirical distributions, based on the largest vertical distance between the two cumulative distribution functions. The Kolmogorov Test is a special case where one distribution function is known, and hence is a Test of goodness-of-fit. Various properties, including power, are discussed. Keywords: distribution-free; nonparametric; goodness-of-fit; power; quantile; distribution function
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Kolmogorov Smirnov Test
Encyclopedia of Biostatistics, 2005Co-Authors: Rand R. WilcoxAbstract:This is a distribution-free method for comparing two empirical distributions, based on the largest vertical distance between the two cumulative distribution functions. The Kolmogorov Test is a special case where one distribution function is known, and hence is a Test of goodness-of-fit. Various properties, including power, are discussed. Keywords: distribution-free; nonparametric; goodness-of-fit; power; quantile; distribution function
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Some practical reasons for reconsidering the Kolmogorov‐Smirnov Test
British Journal of Mathematical and Statistical Psychology, 1997Co-Authors: Rand R. WilcoxAbstract:The Kolmogorov-Smirnov Test is a method for comparing the distributions of two independent groups that has virtually disappeared from applied research and introductory statistics books for the social sciences. The apparent reason is the perception that it has low power compared to methods for comparing means in particular and measures of location in general. However, extant studies comparing the power of the Kolmogorov-Smirnov Test to other methods for comparing means are limited to normal distributions having a common variance. This note points out that, even under a shift model, the Kolmogorov-Smirnov Test not only can have high power relative to methods for comparing robust measures of location, there are situations where it has higher power than methods for comparing robust measurement of location. Some additional features of the Kolmogorov-Smirnov Test are noted, and a simple S-PLUS program for computing the exact significance level is provided. Data from a study on the effects of drinking alcohol are used to illustrate the potential advantage of the Kolmogorov-Smirnov Test.
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some practical reasons for reconsidering the Kolmogorov Smirnov Test
British Journal of Mathematical and Statistical Psychology, 1997Co-Authors: Rand R. WilcoxAbstract:The Kolmogorov-Smirnov Test is a method for comparing the distributions of two independent groups that has virtually disappeared from applied research and introductory statistics books for the social sciences. The apparent reason is the perception that it has low power compared to methods for comparing means in particular and measures of location in general. However, extant studies comparing the power of the Kolmogorov-Smirnov Test to other methods for comparing means are limited to normal distributions having a common variance. This note points out that, even under a shift model, the Kolmogorov-Smirnov Test not only can have high power relative to methods for comparing robust measures of location, there are situations where it has higher power than methods for comparing robust measurement of location. Some additional features of the Kolmogorov-Smirnov Test are noted, and a simple S-PLUS program for computing the exact significance level is provided. Data from a study on the effects of drinking alcohol are used to illustrate the potential advantage of the Kolmogorov-Smirnov Test.
Viliam Makis - One of the best experts on this subject based on the ideXlab platform.
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autoregressive model based gear shaft fault diagnosis using the Kolmogorov Smirnov Test
Journal of Sound and Vibration, 2009Co-Authors: Xiyang Wang, Viliam MakisAbstract:Abstract Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule–Walker equations with the Levinson–Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov–Smirnov Test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov–Smirnov Test.
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Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov Test
Journal of Sound and Vibration, 2009Co-Authors: Xiyang Wang, Viliam MakisAbstract:Abstract Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule–Walker equations with the Levinson–Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov–Smirnov Test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov–Smirnov Test.
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Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov Test
Journal of Sound and Vibration, 2009Co-Authors: Xiyang Wang, Viliam MakisAbstract:Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule-Walker equations with the Levinson-Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov-Smirnov Test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov-Smirnov Test. © 2009 Elsevier Ltd. All rights reserved.
