Mahbanoo Tata

484 citations
9 papers · 364 indexed · h-index 6
Topics
Bayesian Methods and Mixture Models (4 papers)Statistical Distribution Estimation and Applications (4 papers)Probability and Risk Models (3 papers)
Journals
SHILAP Revista de lepidopterologíaFuzzy Sets and SystemsAmerican Mathematical Monthly
Partner nations
Iran

In The Last Decade

Mahbanoo Tata

8 papers receiving 312 citations

Peers

Mahbanoo Tata
Comparison fields: 5 of 55
  • Management Science and Operations Research 261
  • Statistics and Probability 219
  • Control and Systems Engineering 218
  • Computational Theory and Mathematics 44
  • Artificial Intelligence 37
Replace Hassan Mishmast Nehi with:
Hassan Mishmast Nehi Iran
Shuming Wang Japan
Tayebeh Hajjari Iran
Tamás Szántai Hungary
K. David Jamison United States
Chi-Tsuen Yeh Taiwan
P. Sevastianov Poland
Ezzatallah Baloui Jamkhaneh Iran
Mahbanoo Tata relative to Hassan Mishmast Nehi Iran Hassan Mishmast Nehi's profile →
Citations per field
00.5×1.5×2.4×
Hassan Mishmast Nehi · 1×
Citations per year

Countries citing papers authored by Mahbanoo Tata

Since Specialization
Citations

This map shows the geographic impact of Mahbanoo Tata's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mahbanoo Tata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahbanoo Tata more than expected).

Fields of papers citing papers by Mahbanoo Tata

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mahbanoo Tata. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mahbanoo Tata. The network helps show where Mahbanoo Tata may publish in the future.

Co-authorship network of co-authors of Mahbanoo Tata

This figure shows the co-authorship network connecting the top 25 collaborators of Mahbanoo Tata. A scholar is included among the top collaborators of Mahbanoo Tata based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mahbanoo Tata. Mahbanoo Tata is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
1 1
2 5
3 14
4 5
5 16
6 47
7 237
8
FUZZY NUMBER LINEAR PROGRAMMING
8
9 31

About Mahbanoo Tata

Mahbanoo Tata is a scholar working on Statistics and Probability, Management Science and Operations Research and Finance, having authored 9 papers that have together received 364 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (4 papers), Statistical Distribution Estimation and Applications (4 papers) and Probability and Risk Models (3 papers). The work is most often cited by research in Statistics and Probability (219 citations), Management Science and Operations Research (261 citations) and Control and Systems Engineering (218 citations). Mahbanoo Tata has collaborated with scholars based in Iran. Frequent co-authors include Hamid Reza Maleki, M. Mashinchi and G. G. Hamedani. Their work appears in journals such as SHILAP Revista de lepidopterología, Fuzzy Sets and Systems and American Mathematical Monthly.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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