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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Then, we construct examples such that our new approximation result works but its classical and statistical cases do not work.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper using equi-statistical convergence, which is stronger than the usual uniform convergence and statistical uniform convergence, we obtain a general Korovkin type theorem.
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Key Findings & Data:In this paper using equi-statistical convergence, which is stronger than the usual uniform convergence and statistical uniform convergence, we obtain a general Korovkin type theorem.
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Conclusion & Impact:Then, we construct examples such that our new approximation result works but its classical and statistical cases do not work.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Equi-statistical convergence · positive linear operator · Korovkin type theorem
Abstract
In this paper using equi-statistical convergence, which is stronger than the usual uniform convergence and statistical uniform convergence, we obtain a general Korovkin type theorem. Then, we construct examples such that our new approximation result works but its classical and statistical cases do not work.
Özet
In this paper using equi-statistical convergence, which is stronger than the usual uniform convergence and statistical uniform convergence, we obtain a general Korovkin type theorem. Then, we construct examples such that our new approximation result works but its classical and statistical cases do not work.
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Cite This Article
DEMİRCİ, K., KARAKUŞ, S. (2022). Equi-Statistical Extension of the Korovkin Type Approximation Theorem. Turkish Journal of Mathematics, Cilt: 33 - Sayı: 2, 159–168. https://ajindex.com/articles/608549