Ayanendranath Basu

3.2k total citations
97 papers, 1.8k citations indexed

About

Ayanendranath Basu is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence. According to data from OpenAlex, Ayanendranath Basu has authored 97 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Statistics and Probability, 29 papers in Statistics, Probability and Uncertainty and 7 papers in Artificial Intelligence. Recurrent topics in Ayanendranath Basu's work include Advanced Statistical Methods and Models (69 papers), Statistical Methods and Inference (51 papers) and Statistical Methods and Bayesian Inference (25 papers). Ayanendranath Basu is often cited by papers focused on Advanced Statistical Methods and Models (69 papers), Statistical Methods and Inference (51 papers) and Statistical Methods and Bayesian Inference (25 papers). Ayanendranath Basu collaborates with scholars based in India, United States and Spain. Ayanendranath Basu's co-authors include Abhik Ghosh, Kuldip K. Paliwal, Chanseok Park, Bruce G. Lindsay, Hiroyuki Shioya, Leandro Pardo, Abhijit Mandal, Marianthi Markatou, Srabashi Basu and Nirian Martín and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and IEEE Transactions on Information Theory.

In The Last Decade

Ayanendranath Basu

84 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ayanendranath Basu India 17 1.2k 432 301 237 170 97 1.8k
David E. Tyler United States 26 1.5k 1.3× 367 0.8× 483 1.6× 492 2.1× 180 1.1× 63 2.6k
Ronald W. Butler United States 19 792 0.6× 257 0.6× 356 1.2× 99 0.4× 61 0.4× 83 1.6k
Murali Rao United States 15 413 0.3× 226 0.5× 123 0.4× 54 0.2× 100 0.6× 64 2.2k
Stamatis Cambanis United States 24 622 0.5× 175 0.4× 321 1.1× 132 0.6× 106 0.6× 88 1.9k
C. G. Khatri India 22 1.2k 1.0× 165 0.4× 473 1.6× 213 0.9× 78 0.5× 110 2.6k
Vladimir Koltchinskii United States 23 922 0.8× 134 0.3× 925 3.1× 148 0.6× 624 3.7× 52 2.1k
Alessandro Rinaldo United States 20 567 0.5× 82 0.2× 565 1.9× 77 0.3× 103 0.6× 57 1.5k
Carlos Matrán Spain 19 621 0.5× 132 0.3× 453 1.5× 65 0.3× 33 0.2× 55 1.2k
James R. Schott United States 19 745 0.6× 78 0.2× 346 1.1× 130 0.5× 103 0.6× 46 1.6k
Jan Hannig United States 21 939 0.8× 322 0.7× 327 1.1× 43 0.2× 22 0.1× 90 1.6k

Countries citing papers authored by Ayanendranath Basu

Since Specialization
Citations

This map shows the geographic impact of Ayanendranath Basu'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 Ayanendranath Basu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ayanendranath Basu more than expected).

Fields of papers citing papers by Ayanendranath Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ayanendranath Basu. 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 Ayanendranath Basu. The network helps show where Ayanendranath Basu may publish in the future.

Co-authorship network of co-authors of Ayanendranath Basu

This figure shows the co-authorship network connecting the top 25 collaborators of Ayanendranath Basu. A scholar is included among the top collaborators of Ayanendranath Basu 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 Ayanendranath Basu. Ayanendranath Basu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Basu, Ayanendranath, et al.. (2025). Robust inference for linear regression models with possibly skewed error distribution. Journal of Computational and Applied Mathematics. 463. 116502–116502. 1 indexed citations
2.
Mitra, Suman K., et al.. (2025). Blind source separation using novel independence interpretations for bounded support random vector. Journal of the Franklin Institute. 362(12). 107819–107819.
3.
Mukherjee, Adreesh, Gautam Das, Ayanendranath Basu, et al.. (2024). Mental health problems raise the odds of cognitive impairment in COVID-19 survivors. Frontiers in Psychiatry. 15. 1370085–1370085.
4.
Ghosh, Abhik, et al.. (2024). Robust singular value decomposition with application to video surveillance background modelling. Statistics and Computing. 34(5). 1 indexed citations
5.
Pal, Subrata, et al.. (2022). Characterizing the Functional Density Power Divergence Class. IEEE Transactions on Information Theory. 69(2). 1141–1146. 2 indexed citations
6.
Das, Amlan, et al.. (2019). Does the generalized mean have the potential to control outliers?. Communication in Statistics- Theory and Methods. 50(8). 1709–1727. 2 indexed citations
7.
Ghosh, Abhik, Ian R. Harris, Avijit Maji, Ayanendranath Basu, & Leandro Pardo. (2017). A generalized divergence for statistical inference. Bernoulli. 23(4A). 14 indexed citations
8.
Basu, Ayanendranath, Abhijit Mandal, Nirian Martín, & Leandro Pardo. (2015). Generalized Wald-type tests based on minimum density power divergence estimators. Statistics. 50(1). 1–26. 34 indexed citations
9.
Mandal, Abhijit, et al.. (2010). Goodness-of-fit testing in growth curve models: A general approach based on finite differences. Computational Statistics & Data Analysis. 55(2). 1086–1098. 7 indexed citations
10.
Mandal, Abhijit, et al.. (2008). Minimum hellinger distance estimation with inlier modification. 70. 310–322. 3 indexed citations
11.
Basu, Ayanendranath, et al.. (2008). A Natural Goodness-of-Fit Testing Procedure for the Logistic Growth Curve Model. Calcutta Statistical Association Bulletin. 60(1-2). 53–70. 2 indexed citations
12.
Rao, Arni S. R. Srinivasa, Srabashi Basu, Ayanendranath Basu, & Jayanta K. Ghosh. (2005). Parametric models for incubation distribution in presence of left and right censoring. NOT FOUND REPOSITORY (Indian Institute of Science Bangalore). 36(7). 371–384. 2 indexed citations
13.
Basu, Ayanendranath, et al.. (2005). On the unlinking conjecture of independent polynomial functions. Journal of Multivariate Analysis. 97(6). 1355–1360. 2 indexed citations
14.
Mitra, Sinjini, Srabashi Basu, & Ayanendranath Basu. (2000). Exact minimum disparity inference in complex multinomial models. METRON. 167–185.
15.
Bhattacharya, Bhaskar & Ayanendranath Basu. (1996). Robust estimates of ordered means in normal models. Journal of Statistical Computation and Simulation. 54(1-3). 165–175.
16.
Park, Chanseok, Ayanendranath Basu, & Srabashi Basu. (1995). Robust minimum distance inference based on combined distances. Communications in Statistics - Simulation and Computation. 24(3). 653–673. 8 indexed citations
17.
Basu, Srabashi & Ayanendranath Basu. (1995). Comparison of several goodness‐of‐fit tests for the kappa statistic based on exact power and coverage probability. Statistics in Medicine. 14(4). 347–356. 8 indexed citations
18.
Harris, Ian R. & Ayanendranath Basu. (1994). Hellinger distance as a penalized log likelihood. Communications in Statistics - Simulation and Computation. 23(4). 1097–1113. 17 indexed citations
19.
Basu, Ayanendranath & Ian R. Harris. (1994). Robust predictive distributions for exponential families. Biometrika. 81(4). 790–794. 7 indexed citations
20.
Basu, Ayanendranath. (1984). 13. Stochastic differential equations. Stochastic Processes and their Applications. 18(2). 230–230. 3 indexed citations

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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