The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Michael Eliasziw - One of the best experts on this subject based on the ideXlab platform.
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a general goodness of fit approach for inference procedures concerning the kappa statistic
Statistics in Medicine, 2001Co-Authors: Mekibib Altaye, Allan Donner, Michael EliasziwAbstract:The kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories.
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a goodness of fit approach to inference procedures for the kappa statistic confidence interval construction significance Testing and sample size estimation
Statistics in Medicine, 1992Co-Authors: Allan Donner, Michael EliasziwAbstract:We propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance-Testing and the planning of corresponding sample size requirements.
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A goodness‐of‐fit approach to inference procedures for the kappa statistic: Confidence interval construction, significance‐Testing and sample size estimation
Statistics in medicine, 1992Co-Authors: Allan Donner, Michael EliasziwAbstract:We propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance-Testing and the planning of corresponding sample size requirements.
Mickael Albertus - One of the best experts on this subject based on the ideXlab platform.
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Raking-ratio empirical process with auxiliary information learning
ESAIM: Probability and Statistics, 2020Co-Authors: Mickael AlbertusAbstract:The raking-ratio method is a statistical and computational method which adjusts the empirical measure to match the true probability of sets of a finite partition. The asymptotic behavior of the raking-ratio empirical process indexed by a class of functions is studied when the auxiliary information is given by estimates. These estimates are supposed to result from the learning of the probability of sets of partitions from another sample larger than the sample of the statistician, as in the case of two-stage sampling surveys. Under some metric entropy hypothesis and conditions on the size of the information source sample, the strong approximation of this process and in particular the weak convergence are established. Under these conditions, the asymptotic behavior of the new process is the same as the classical raking-ratio empirical process. Some possible statistical applications of these results are also given, like the strengthening of the Z-Test and the Chi-Square goodness of fit Test.
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Raking-ratio empirical process with auxiliary information learning
2019Co-Authors: Mickael AlbertusAbstract:The raking-ratio method is a statistical and computational method which adjusts the empirical measure to match the true probability of sets in a finite partition. We study the asymptotic behavior of the raking-ratio empirical process indexed by a class of functions when the auxiliary information is given by the learning of the probability of sets in partitions from another sample larger than the sample of the statistician. Under some metric entropy hypothesis and conditions on the size of the independent samples, we establish the strong approximation of this process with estimated auxiliary information and show in particular that weak convergence is the same as the classical raking-ratio empirical process. We also give possible statistical applications of these results like strengthening the Z-Test and the Chi-Square goodness of fit Test.
Allan Donner - One of the best experts on this subject based on the ideXlab platform.
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a general goodness of fit approach for inference procedures concerning the kappa statistic
Statistics in Medicine, 2001Co-Authors: Mekibib Altaye, Allan Donner, Michael EliasziwAbstract:The kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories.
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a goodness of fit approach to inference procedures for the kappa statistic confidence interval construction significance Testing and sample size estimation
Statistics in Medicine, 1992Co-Authors: Allan Donner, Michael EliasziwAbstract:We propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance-Testing and the planning of corresponding sample size requirements.
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A goodness‐of‐fit approach to inference procedures for the kappa statistic: Confidence interval construction, significance‐Testing and sample size estimation
Statistics in medicine, 1992Co-Authors: Allan Donner, Michael EliasziwAbstract:We propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a Chi-Square Goodness-of-Fit Test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance-Testing and the planning of corresponding sample size requirements.
L.b. Milstein - One of the best experts on this subject based on the ideXlab platform.
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Statistical modeling of W-CDMA signals for use over frequency-selective multipath channels
IEEE Transactions on Communications, 2004Co-Authors: Cheol-sung Kim, L.b. MilsteinAbstract:This letter presents a mathematical model for the statistics of wideband code-division multiple-access signals which experience multipath fading. The statistics are obtained from numerically generated signals, and are then modeled with an offset noncentral chi distribution with three degrees of freedom. The mathematical model is Tested against the numerically generated data by the use of a Chi-Square Goodness-of-Fit Test.
Jun Feng - One of the best experts on this subject based on the ideXlab platform.
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chi square goodness of fit Test of 3d point correspondence for model similarity measure and analysis
Conference on Image and Video Retrieval, 2005Co-Authors: Jun Feng, Horace H. S. IpAbstract:Accurate and robust correspondence calculations are the pre-requisite step in many 3D model query and retrieval process. However, the correspondence problem is particularly difficult for 3D biomedical model surfaces, especially for roundish and approximate symmetric organs such as liver, stomach, kidney etc. In this paper, we define a new feature representation called the Neighborhood Relative Angle context Distribution (NRACD) for each vertex and, based upon it, we apply the Chi-Square Goodness-of-Fit Test to establish 3D point correspondence. We further define the similarities between correspondence ready models by Chi-Square Test statistic values. The experimental results demonstrate that this approach is efficient and robust for surface point matching and is particularly applicable to the retrieval and analysis of 3D deformable objects.
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CIVR - Chi-Square Goodness-of-Fit Test of 3d point correspondence for model similarity measure and analysis
Lecture Notes in Computer Science, 2005Co-Authors: Jun FengAbstract:Accurate and robust correspondence calculations are the pre-requisite step in many 3D model query and retrieval process. However, the correspondence problem is particularly difficult for 3D biomedical model surfaces, especially for roundish and approximate symmetric organs such as liver, stomach, kidney etc. In this paper, we define a new feature representation called the Neighborhood Relative Angle context Distribution (NRACD) for each vertex and, based upon it, we apply the Chi-Square Goodness-of-Fit Test to establish 3D point correspondence. We further define the similarities between correspondence ready models by Chi-Square Test statistic values. The experimental results demonstrate that this approach is efficient and robust for surface point matching and is particularly applicable to the retrieval and analysis of 3D deformable objects.
