The limiting distribution of combining the <i>t</i> and Wilcoxon rank sum tests

Kitani, Masato; Murakami, Hidetoshi

doi:10.1080/02331888.2020.1809662

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…To further validate this finding, a series of Wilcoxon t -tests (Kitani and Murakami, 2020) were conducted to specifically compare ranking of relational social capital items to both cognitive and structural items independently. The results further support the initial findings.…”

Section: Resultsmentioning

confidence: 99%

How family firm advisors understand their clients: a mixed-methods analysis of social capital signaling in web-based marketing

Randolph,

Kushins,

Gala

2023

JFBM

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PurposeDespite similarities, research across family business and business advising forwards contradictory conclusions when considering family business advising. The authors seek to integrate these literature and in doing so uncover both the hurdles facing family business advisors attempting to adapt tools developed in corporate advising to the family business context as well as the potential for greater integration of these streams in ways that contribute to both family business and advising research and practice.Design/methodology/approachPrimary data were collected both in the form of a survey questionnaire and website marketing content. In the survey, 47 family business advisors evaluated the distinctiveness of their family business clients across structural, cognitive and relational social capital dimensions. Motivated by unexpected findings, a content analysis of advisor websites uncovered specific marketing themes that illustrate the divides between family business advising and scholarship.FindingsFamily business advisors reliably acknowledge structural and cognitive social capital as preeminently characterizing the distinctiveness of their family business clients. Expanding on this, the authors’ findings suggest that the urgency signaled in advisor marketing via their websites may inspire tactics misaligned with the long-term time horizon typically characterizing family businesses strategy.Originality/valueThe few family business advising studies that exist predominantly consider post-hoc evaluation of advising by family business clients. The primary data the authors collect are unique in the literature in that the data detail how family business advisors perceive and engage with potential clients.

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Section: Resultsmentioning

confidence: 99%

How family firm advisors understand their clients: a mixed-methods analysis of social capital signaling in web-based marketing

Randolph,

Kushins,

Gala

2023

JFBM

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“…The established marker peaks could be used as targets for the screening, detection, prognosis, and monitoring of diseases. For the establishment of marker peaks, we have applied the statistical algorithms like t ‐test, ANOVA test, 46 fisher's exact test, 47 and Wilcoxon rank sum test, 48 as well as feature selection algorithms like information gain‐based feature selection and correlation‐based feature selection 49,50 . Marker peaks can also be selected by using machine learning algorithms like least absolute shrinkage and selection operator, partial least squares‐discriminant analysis, and recursive feature elimination with cross validation 51,52 …”

Section: Techniques and Algorithmsmentioning

confidence: 99%

Algorithms push forward the application of MALDI–TOF mass fingerprinting in rapid precise diagnosis

Zhu

Girault

2023

VIEW

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Macromolecular mass fingerprinting using matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry is a quick tool for biochemical analysis. In combination with algorithms for automated data analysis and interpretation, it has wide application potentials in life science, especially for diagnostic purposes. This review aims to illustrate recent advances of this analytical tool, focusing on the improvement of the experimental techniques, the data analysis algorithms, and more importantly the applications in rapid precise diagnosis.

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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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The limiting distribution of combining the t and Wilcoxon rank sum tests

Cited by 8 publications

References 15 publications

How family firm advisors understand their clients: a mixed-methods analysis of social capital signaling in web-based marketing

How family firm advisors understand their clients: a mixed-methods analysis of social capital signaling in web-based marketing

Algorithms push forward the application of MALDI–TOF mass fingerprinting in rapid precise diagnosis

An adaptive surrogate-assisted particle swarm optimization for expensive problems

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