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Using a nonparametric kernel smoothing method we identified the most sensitive items of the SPIN.
After constructing our propensity model, we used PSMATCH2 (version 3.1.5) [ 23] to match controls to participants with diabetes using a nonparametric kernel matching estimator.
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The estimation of the OCCs of the expected total score of the three PANSS subscales was made using a nonparametric (Gaussian) kernel smoothing technique [ 19, 24] illustrated above.
LLORMA [7, 8] uses a nonparametric kernel smoothing method to search nearest neighbors; WEMAREC [26] employs Bregman co-clustering [30] techniques to partition the original matrix.
The PDFs are computed using a nonparametric technique with a Gaussian kernel function for smoothing.
We would recommend using a nonparametric method such as the Kernel Density Estimation together with fast evaluation algorithms (e.g., the Fast Gauss Transform [ 41]) such that missing values can be replaced by good estimates with little computational power even for large trials with numerous factors.
Data were analyzed using a nonparametric test.
It was designed by using a nonparametric Bayesian approach.
Intragroup comparisons of continuous data were performed using a nonparametric Wilcoxon test for repeated measures.
Histopathology and immunohistochemistry data were analyzed using a nonparametric Mann–Whitney U test.
Means were compared using a nonparametric test.
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