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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Also, we study the rates of statistical relatively A−summability of positive linear operators.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we introduce the concept of statistically relatively A-summability.
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Key Findings & Data:Based upon this definition and A-statistically relatively uniform convergence for double sequences of functions, we prove a Korovkin-type approximation theorem and give a strong example.
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Conclusion & Impact:Also, we study the rates of statistical relatively A−summability of positive linear operators.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Statistical relatively convergence · statistical A−summability · the Korovkin theorem
Özet
In this paper, we introduce the concept of statistically relatively A-summability. Based upon this definition and A-statistically relatively uniform convergence for double sequences of functions, we prove a Korovkin-type approximation theorem and give a strong example. Also, we study the rates of statistical relatively A−summability of positive linear operators.
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Cite This Article
DİRİK, F., ŞAHİN, P. O. (2017). Statistically Relatively A−summability of Convergence of Double Sequences of Positive Linear Operators. Sinop Üniversitesi Fen Bilimleri Dergisi, Cilt: 2 - Sayı: 1, 59–66. https://ajindex.com/articles/415172