The sign test and the signed‐rank test for interval‐valued data

Grzegorzewski, Przemysław; Śpiewak, Martyna

doi:10.1002/int.22134

Cited by 30 publications

(15 citation statements)

References 47 publications

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“…In this case, the methods using the fuzzy logic are applied for estimation and forecasting of the energy 23 . Introduced time series methods using fuzzy logic 24 – 26 . Introduced an analysis method using the fuzzy logic 27 .…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Forecasting of the wind speed under uncertainty

Aslam

2020

Sci Rep

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In this paper, the semi-average method under neutrosophic statistics is introduced. The trend regression line for the semi-average method is given in the presence of Neutrosophy in the data. The application of the semi-average method under indeterminacy is given with the help of wind speed data. The efficiency of the semi-average method under the neutrosophic statistics is discussed over the semi-average method under classical statistics. From the analysis, it is concluded that the proposed method is effective, informative, and flexible for the forecasting of wind speed.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Forecasting of the wind speed under uncertainty

Aslam

2020

Sci Rep

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“…In cases that Kruskal-Wallis test showed a significant difference, pair-wise comparisons with Bonferroni correction (adj sig) and Mann-Whitney test were conducted as post-hoc analysis to determine which group(s) differ (Roni et al, 2020). Moreover, in order to evaluate if there are significant differences before and after teaching intervention, Wilcoxon signed ranks tests were applied for every group (Grzegorzewski & Śpiewak, 2019). Effect size (r = Z/N1/2) was calculated for which a significant difference was revealed (Tomczak & Tomczak 2014;Fritz et al, 2011).…”

Section: Groups' Achievement Comparisonmentioning

confidence: 99%

Teaching Chemistry with Arduino Experiments in a Mixed Virtual-Physical Learning Environment

2021

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A study with K-9 Greek students was conducted in order to evaluate how the declarative knowledge acquisition was affected by incorporating Arduino experiments in secondary Chemistry Education. A Digital Application (DA) that blends the use of the Arduino sensors' experiments with digital educational material, including Virtual Labs (VLs), was constructed from scratch to be used through the Interactive Board (IB) as a learning tool by three different student groups (N = 154). In the first stage of the learning process, all groups used only the digital material of the DA. In the second stage, the three groups used different learning tools of the DA. Through the IB, the first group used Arduino experiments, the second one the VLs, and the third only static visualizations. A pre-to post-test statistical analysis demonstrated that the first two groups were equivalent in regard to achievement in declarative knowledge tests and of a higher level than the third group. Therefore, it can be concluded that conducting Arduino experiments in a mixed virtual-physical environment results in equivalent learning gains in declarative knowledge as those attained by using VL experimentation through the IB.

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“…For these reasons, some authors have considered approaches of fuzzy statistics to deal appropriately with fuzziness in data and hypotheses formulation. In the literature of fuzzy hypothesis testing, there are a few publications considering the sign test in fuzzy environments (see Chukhrova and Johannssen, 3 for a systematic review): Fuzzy/interval‐valued data caused by the imprecision of observations (see Grzegorzewski, 4,5 Grzegorzewski and Spiewak, 6‐8 Hesamian and Chachi, 9 Hesamian and Taheri, 10 Kahraman et al, 11 Momeni and Sadeghpour‐Gildeh, 12 and Shams and Hesamian 13 ), that is, fuzzy data as perception of a crisp but unobservable random variable (so called epistemic perspective, see Couso and Dubois 14 ), or fuzzy set‐/interval‐valued random variables (so called ontic perspective) (see Grzegorzewski and Spiewak 6,7 ). Fuzzy/interval‐valued hypotheses caused by fuzzy quantiles like the fuzzy median (see Grzegorzewski and Spiewak 6,7 ) or imprecision of linguistic statements on quantiles (see Hesamian and Chachi, 9 Hesamian and Taheri, 10 Momeni and Sadeghpour‐Gildeh, 12 and Shams and Hesamian 13 ).…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy/interval‐valued data caused by the imprecision of observations (see Grzegorzewski, 4,5 Grzegorzewski and Spiewak, 6‐8 Hesamian and Chachi, 9 Hesamian and Taheri, 10 Kahraman et al, 11 Momeni and Sadeghpour‐Gildeh, 12 and Shams and Hesamian 13 ), that is, fuzzy data as perception of a crisp but unobservable random variable (so called epistemic perspective, see Couso and Dubois 14 ), or fuzzy set‐/interval‐valued random variables (so called ontic perspective) (see Grzegorzewski and Spiewak 6,7 ).…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19

Chukhrova

Johannssen

2021

Int J Intell Syst

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The sign test is one of the most popular nonparametric tests for location problems and allows testing for any quantile of a population. However, the common sign test has serious drawbacks such as loss of information by considering solely signs of observations but not their magnitudes, various problems related to handling of ties in the data, and the lack of embedding uncertainty regarding the fraction of underlying quantile. To address these issues, we present an extended sign test based on fuzzy categories and fuzzy formulated hypotheses that improves the generality, versatility, and practicability of the common sign test. This generalized test procedure is neat in theory and practice and avoids disadvantages that are often associated with fuzzy tests (e.g., a considerably higher complexity of the underlying model, a fuzzy test decision, and a possibilistic instead of a probabilistic interpretation of test results). In addition, we perform a comprehensive case study on COVID‐19 in HIV‐infected individuals with a focus on human body temperature and related measurement problems. The results of the study clearly indicate that fuzzy categories and fuzzy hypotheses improve the performance of the sign test.

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The sign test and the signed‐rank test for interval‐valued data

Cited by 30 publications

References 47 publications

RETRACTED ARTICLE: Forecasting of the wind speed under uncertainty

RETRACTED ARTICLE: Forecasting of the wind speed under uncertainty

Teaching Chemistry with Arduino Experiments in a Mixed Virtual-Physical Learning Environment

Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19

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