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The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm.
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Analyses have been carried out on a total of 302 previously published tests using a nonparametric regression procedure known as multivariate adaptive regression splines (MARS), to assess the capacity energy required to trigger liquefaction in sand and silty sands.
Data were analyzed using a nonparametric test.
It was designed by using a nonparametric Bayesian approach.
The PDFs are computed using a nonparametric technique with a Gaussian kernel function for smoothing.
Intragroup comparisons of continuous data were performed using a nonparametric Wilcoxon test for repeated measures.
The model stability was validated using a nonparametric bootstrap (100 samples with replacement).
Histopathology and immunohistochemistry data were analyzed using a nonparametric Mann–Whitney U test.
The responses were analysed using a nonparametric approach and confirmed our initial observations.
Using a nonparametric kernel smoothing method we identified the most sensitive items of the SPIN.
The conditional distributions were computed using a simple Gaussian distribution or using a nonparametric density estimator.
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