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An Analysis of Variance of the Pantheon+ Dataset: Systematics in the Covariance Matrix?
Universe, 2022We investigate the statistics of the available Pantheon+ dataset. Noticing that the χ2 value for the best-fit ΛCDM model to the real data is small, we quantify how significant its smallness is by calculating the distribution of χ2 values for the best-fit
R. Keeley, A. Shafieloo, B. L’Huillier
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Analysis of covariance under neutrosophic statistics
Journal of Statistical Computation and Simulation, 2022Analysis of covariance (ANCOVA) is a widely used statistical analysis technique in numerous experimental studies. The existing ANCOVA test cannot be applied when the sample is taken from a population with imprecise and uncertain data.
Abdulrahman AlAita +1 more
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Biometrics, 1982
The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method.
Birch, Jeffrey B., Myers, Raymond H.
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The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method.
Birch, Jeffrey B., Myers, Raymond H.
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Analysis of Covariance Structures
Psychometrika, 1966A general method is presented for estimating variance components when the experimental design has one random way of classification and a possibly unbalanced fixed classification. The procedure operates on a sample covariance matrix in which the fixed classes play the role of variables and the random classes correspond to observations.
R D, Bock, R E, Bargmann
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Analysis of Covariance Algorithms
Biometrics, 1982Computational algorithms specific to the analysis of covariance are discussed for the treatment of both balanced and unbalanced data. The use of covariance algorithms in the solution of missing data problems is also considered.
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