100 Statistical Tests

Kanji, Gopal K.

doi:10.4135/9781849208499

Cited by 347 publications

(244 citation statements)

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“…(4) Kendall's coefficient of rank correlation t (Kanji, 1999), which was used to quantify the agreement between the simulated and estimated from EC measurements seasonal patterns of GPP. The Kendall coefficient measured the tendency coherence between predicted and observed GPP by comparing the ranks assigned to successive pairs.…”

Section: Nonlinear Optimization and Statistical Analysismentioning

confidence: 99%

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

Yuan

Liu

Zhou

et al. 2007

Agricultural and Forest Meteorology

633

441

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Section: Nonlinear Optimization and Statistical Analysismentioning

confidence: 99%

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

Yuan

Liu

Zhou

et al. 2007

Agricultural and Forest Meteorology

633

441

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“…To provide an aggregated perspective on the performance of the Diff extension against the other methods, we employ the Friedman's test for multiple achievements of multiple subjects [15] on the best-of-run fitness. The p-value 1.41 × 10 −9 strongly indicates that at least one method performs significantly different from the remaining ones.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

Krawiec

Liskowski

2017

Foundations of Computing and Decision Sciences

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Abstract. Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP's runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

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“…Although marginal reduction in the counts was obtained with the augmented cost compared to PCC or MSD alone, significant reduction was obtained compared to those of MI or NMI alone. To obtain a measure of the statistical significance of reduction in the counts for the augmented costs (i.e., AMI and ANMI), we performed the paired t-test with onetail at the 95% confidence level against other costs [Kanji et al 1993]. The registration results using AMI were found to be better than those of MSD, MI, and NMI significantly (P=0.0086, 0.0027, and 0.0035) and so were the results using ANMI (P=0.0068, 0.0021, and 0.0027).…”

Section: D3 Two-mentioning

confidence: 99%

Co‐registration of in vivo human MRI brain images to postmortem histological microscopic images

Singh

Rajagopalan

Kim

et al. 2008

Int J Imaging Syst Tech

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Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRIpostmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects.

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100 Statistical Tests

Cited by 347 publications

References 0 publications

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

Co‐registration of in vivo human MRI brain images to postmortem histological microscopic images

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