Two-Sample Median Test for Interval-Valued Data

Grzegorzewski, Przemysław; Śpiewak, Martyna

doi:10.1007/978-3-030-47024-1_13

Cited by 2 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 summarizes the data. For more information see [18]. Data frequencies of melting points of two alloys are shown in Table 3.…”

Section: Example 41mentioning

confidence: 99%

See 1 more Smart Citation

A New Mood’s Median Test for Imprecise Data

AlAita,

Aslam,

Smarandache

2024

Int. J. Anal. Appl.

View full text Add to dashboard Cite

The existing Mood’s median test is a non-parametric test used to compare two or more sample data sets whether they are from the same population or not. There can be no application of this test to uncertain and indeterminate data. Therefore, it is necessary to find a generalization of this test that will enable us to apply it in uncertain environments. This study will present a new approach that utilizes neutrosophic statistics to apply Mood’s median test. The approach involves defining hypotheses, determining a decision rule, and performing the test in an uncertain environment. An evaluation of the performance of the proposed test will be conducted using numerical examples. The results reveal that the proposed test under neutrosophic statistics is more informative, efficient, and flexible than the existing test under classical statistics in the presence of uncertain data.

show abstract

“…Table 2 summarizes the data. For more information see [18]. Data frequencies of melting points of two alloys are shown in Table 3.…”

Section: Example 41mentioning

confidence: 99%

“…Miari, et al [17] suggested single valued neutrosophic Kruskal-Wallis and Mann Whitney tests. The median test for interval-valued data was highlighted by Grzegorzewski and Śpiewak [18]. Aslam and Saleem [19] introduced the F-test of testing linearity under neutrosophic statistics.…”

Section: Introductionmentioning

confidence: 99%

A New Mood’s Median Test for Imprecise Data

AlAita,

Aslam,

Smarandache

2024

Int. J. Anal. Appl.

View full text Add to dashboard Cite

show abstract

“…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%

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

Chukhrova

Johannssen

2021

Int J Intell Syst

View full text Add to dashboard Cite

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.

show abstract

Two-Sample Median Test for Interval-Valued Data

Cited by 2 publications

References 9 publications

A New Mood’s Median Test for Imprecise Data

A New Mood’s Median Test for Imprecise Data

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

Contact Info

Product

Resources

About