Maximum-type tests for high-dimensional regression coefficients using Wilcoxon scores

Xu, Kai; Zhou, Yeqing

doi:10.1016/j.jspi.2020.06.011

Cited by 2 publications

(1 citation statement)

References 43 publications

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“…In addition, the results reported in Table 3 were analyzed by a Friedman test (López-Vázquez et al 2019) at the significance level of 0.05   , and p-value = 0.000 < 0.05 was obtained; this indicates that the four algorithms had significant differences. Therefore, a Wilcoxon test (Xu et al 2020) was conducted at the significance level of 0.05   to further compare the differences between the other three algorithms and ASAPSO. The last row of Table 3 lists the results of the Wilcoxon test, and the p-values were respectively 0.023, 0.000, and 0.004, which are all less than 0.05; this means that the ASAPSO algorithm performed significantly better than the other three algorithms.…”

Section: Low-dimensional Problemsmentioning

confidence: 99%

An adaptive surrogate-assisted particle swarm optimization for expensive problems

2021

Soft Comput

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To solve engineering problems with evolutionary algorithms, many expensive objective function evaluations (FEs) are required. To alleviate this difficulty, the surrogateassisted evolutionary algorithm (SAEA) has attracted increasingly more attention in both academia and industry. The existing SAEAs depend on the quantity and quality of the original samples, and it is difficult for them to yield satisfactory solutions within the limited number of FEs. Moreover, these methods easily fall into local optima as the dimension increases. To address these problems, this paper proposes an adaptive surrogate-assisted particle swarm optimization (ASAPSO) algorithm. In the proposed algorithm, an adaptive surrogate selection method that depends on the comparison between the best existing solution and the latest obtained solution is suggested to ensure the effectiveness of the optimization operations and improve the computational efficiency. Additionally, a model output criterion based on the standard deviation is suggested to improve the robustness and stability of the ensemble model. To verify the performance of the proposed algorithm, 10 benchmark functions with different modalities from 10 to 50 dimensions are tested, and the results are compared with those of five state-of-the-art SAEAs. The experimental results indicate that the proposed algorithm performs well for most benchmark functions within the limited number of FEs. The performance of the proposed algorithm in solving engineering problems is verified by applying the algorithm to the PX oxidation process.

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Section: Low-dimensional Problemsmentioning

confidence: 99%