Efficiency comparison of the Wilcoxon tests in paired and independent survey samples

Baringhaus, Ludwig; Gaigall, Daniel

doi:10.1007/s00184-018-0661-4

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Thus, the experimentation of this research entails the analysis of more than 4 M results. The conclusions derived from them are contrasted using the statistical tests recommended in the specialized literature [25]. Specifically, Wilcoxon's test [25] is used for rejecting the null hypothesis of the equality of means in pairwise comparisons, implying the superiority of one of the methods.…”

Section: Methodology Of Analysismentioning

confidence: 99%

“…The conclusions derived from them are contrasted using the statistical tests recommended in the specialized literature [25]. Specifically, Wilcoxon's test [25] is used for rejecting the null hypothesis of the equality of means in pairwise comparisons, implying the superiority of one of the methods. A significance level α = 0.1 is assumed in this paper.…”

Section: Methodology Of Analysismentioning

confidence: 99%

“…The partitionings have been repeated thousands of times with each approach, leading to a total of more than 4 M results to analyze. The statistical tests recommended in the literature have been employed to contrast the conclusions derived from the analysis of test performance results and target shift between the training and test sets [25]. A webpage with the details of the experimentation, datasets, additional results and plots can be accessed at https://joseasaezm.github.io/scvreg/ (accessed on 29 June 2022).…”

Section: Introductionmentioning

confidence: 99%

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Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Sáez

Romero‐Béjar

2022

Mathematics

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Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results.

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Section: Methodology Of Analysismentioning

confidence: 99%

Section: Methodology Of Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Sáez

Romero‐Béjar

2022

Mathematics

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“…The test has been performed against the null hypothesis that there is no difference in their population means. Under this null hypothesis, the Wilcoxon rank sum test (Wilcoxon-Mann-Whitney test) [45] has been performed (1) Initialize the population and calculate the fitness so as to find the optimal position of particle i and the global optimal position (2) Set evolution times and initialize the current iteration g = 1 (3) do (4) Update the velocity vector of every particle according to Eq. (6) (5) Update particle position according to the position equation Eq.…”

Section: Assembly Posture Optimization and Deviation Calculationmentioning

confidence: 99%

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

Zhang

Sherar

et al. 2018

Mathematical Problems in Engineering

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Geometric deviations inevitably occur in product manufacturing and seriously affect the assembly quality and product functionality. Assembly simulations on the basis of computer-aided design (CAD) package could imitate the assembly process and thus find out the design deficiencies and detect the assemblability of the components. Although lots of researches have been done on the prediction of assembly variation considering the geometric errors, most of them only simplify the geometric variation as orientation and position deviation rather than the manufacturing deformation. However, in machinery manufacturing, even if the manufacturing defects are limited, they could propagate and accumulate through components and lead to a noncompliant assembly. Recently, many point-based models have been applied to assembly simulation; however they are mainly interested in simulating the resulting positions of the assembled parts and lack the consideration of the postprocessing after positioning. This paper enriches the complete assembly simulation process based on skin model and presents a simple and effective posture evaluation and optimization method. The studied approach includes a software algorithm applied to evaluate the contact state of the assembly parts and a mathematical model based on the particle swarm optimization to acquire the optimal assembly posture. To verify the efficiency and feasibility of the proposed method, a case study on the aircraft wing box scaling model assembly is performed.

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“…Wilcoxon's test [61] will be employed to properly analyze both accuracy and ELA results and detect differences between two sample means, as suggested in the literature [62]. For each noise model and level, the baseline algorithm and that using bagging will be compared, and the corresponding p-values will be obtained.…”

mentioning

confidence: 99%

On the Suitability of Bagging-Based Ensembles with Borderline Label Noise

Sáez

Romero‐Béjar

2022

Mathematics

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Real-world classification data usually contain noise, which can affect the accuracy of the models and their complexity. In this context, an interesting approach to reduce the effects of noise is building ensembles of classifiers, which traditionally have been credited with the ability to tackle difficult problems. Among the alternatives to build ensembles with noisy data, bagging has shown some potential in the specialized literature. However, existing works in this field are limited and only focus on the study of noise based on a random mislabeling, which is unlikely to occur in real-world applications. Recent research shows that other types of noise, such as that occurring at class boundaries, are more common and challenging for classification algorithms. This paper delves into the analysis of the usage of bagging techniques in these complex problems, in which noise affects the decision boundaries among classes. In order to investigate whether bagging is able to reduce the impact of borderline noise, an experimental study is carried out considering a large number of datasets with different noise levels, and several noise models and classification algorithms. The results obtained reflect that bagging obtains a better accuracy and robustness than the individual models with this complex type of noise. The highest improvements in average accuracy are around 2–4% and are generally found at medium-high noise levels (from 15–20% onwards). The partial consideration of noisy samples when creating the subsamples from the original training set in bagging can make it so that only some parts of the decision boundaries among classes are impaired when building each model, reducing the impact of noise in the global system.

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Efficiency comparison of the Wilcoxon tests in paired and independent survey samples

Cited by 7 publications

References 17 publications

Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Posture Optimization Algorithm for Large Structure Assemblies Based on Skin Model

On the Suitability of Bagging-Based Ensembles with Borderline Label Noise

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