Snigdhansu Chatterjee

2.5k total citations · 1 hit paper
62 papers, 1.4k citations indexed

About

Snigdhansu Chatterjee is a scholar working on Statistics and Probability, Global and Planetary Change and Soil Science. According to data from OpenAlex, Snigdhansu Chatterjee has authored 62 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Statistics and Probability, 12 papers in Global and Planetary Change and 7 papers in Soil Science. Recurrent topics in Snigdhansu Chatterjee's work include Statistical Methods and Inference (15 papers), Climate variability and models (12 papers) and Advanced Statistical Methods and Models (12 papers). Snigdhansu Chatterjee is often cited by papers focused on Statistical Methods and Inference (15 papers), Climate variability and models (12 papers) and Advanced Statistical Methods and Models (12 papers). Snigdhansu Chatterjee collaborates with scholars based in United States, India and United Kingdom. Snigdhansu Chatterjee's co-authors include D. K. Ray, Alexander V. Prishchepov, Michael Clark, Paul West, James Gerber, Peihua Qiu, Nitai D. Mukhopadhyay, Arup Bose, Auroop R. Ganguly and Partha Lahiri and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Snigdhansu Chatterjee

59 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Climate change has likely already affected global food production

2019 602 citations Snigdhansu Chatterjee et al. PLoS ONE profile →

Peers

Snigdhansu Chatterjee

PS

EEBS

SP

GPC

MB

Marcin Kozak Poland

Kevin McConway United Kingdom

Marcia L. Gumpertz United States

J. H. Matis United States

José García García Spain

Jérôme Pagès France

Min Zhu China

Hana Ševčíková United States

J. A. John United Kingdom

A. P. Verbyla Australia

Marcin Kozak Poland

Snigdhansu Chatterjee

640 ×1.9

PS

177 ×0.7

EEBS

229 ×1.1

SP

67 ×0.3

GPC

123 ×0.9

MB

Citations per year, relative to Snigdhansu Chatterjee Snigdhansu Chatterjee (= 1×) peers Marcin Kozak

Countries citing papers authored by Snigdhansu Chatterjee

Since Specialization

Citations

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

Fields of papers citing papers by Snigdhansu Chatterjee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Snigdhansu Chatterjee

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

All Works

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20 of 20 papers shown

1.

Torres, José Antonio Garrido, et al.. (2024). Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning. The Journal of Chemical Physics. 160(20). 4 indexed citations

2.

Chatterjee, Snigdhansu, et al.. (2021). Winsorization for Robust Bayesian Neural Networks. Entropy. 23(11). 1546–1546. 11 indexed citations

3.

Cooley, Daniel, et al.. (2019). New Exploratory Tools for Extremal Dependence: $$\chi $$ χ Networks and Annual Extremal Networks. Open MIND. 6 indexed citations

4.

Ermagun, Alireza, Snigdhansu Chatterjee, & David Levinson. (2017). Using temporal detrending to observe the spatial correlation of traffic. PLoS ONE. 12(5). e0176853–e0176853. 27 indexed citations

5.

Chatterjee, Snigdhansu. (2017). State-wise comparative analysis of different cost components and factor productivity of paddy in India during last decade.. 2(1). 83–97. 1 indexed citations

6.

Chattopadhyay, A., et al.. (2016). Selecting Parental Lines among Cultivated and Wild Species of Okra for Hybridization Aiming at YVMV Disease Resistance. Journal of Agricultural Science and Technology. 18(3). 751–762. 17 indexed citations

7.

Chatterjee, Snigdhansu, et al.. (2015). Methodology of Identification and Characterization of Farming Systems in Irrigated Agriculture: Case Study in West Bengal State of India. Journal of Agricultural Science and Technology. 17(5). 1127–1140. 19 indexed citations

8.

Atluri, Gowtham, et al.. (2015). Tripoles: A New Class of Climate Teleconnections. University of Minnesota Digital Conservancy (University of Minnesota).

9.

Louzoun, Yoram, et al.. (2015). Power Laws for Heavy-Tailed Distributions: Modeling Allele and Haplotype Diversity for the National Marrow Donor Program. PLoS Computational Biology. 11(4). e1004204–e1004204. 20 indexed citations

10.

Chatterjee, Snigdhansu, et al.. (2014). Logit-normal mixed model for Indian monsoon precipitation. Nonlinear processes in geophysics. 21(5). 939–953. 2 indexed citations

11.

Chatterjee, Snigdhansu, et al.. (2012). Input use and technological change in cotton cultivation in different states of India.. Journal of Crop and Weed. 8(1). 95–97. 2 indexed citations

12.

Steinhaeuser, Karsten, et al.. (2012). Sparse group lasso. 3 indexed citations

13.

Kawale, Jaya, et al.. (2011). ANOMALY CONSTRUCTION IN CLIMATE DATA: ISSUES AND CHALLENGES. Open MIND. 189–203. 15 indexed citations

14.

Mukhopadhyay, Nitai D. & Snigdhansu Chatterjee. (2010). High dimensional data analysis using multivariate generalized spatial quantiles. Journal of Multivariate Analysis. 102(4). 768–780. 5 indexed citations

15.

Chatterjee, Snigdhansu, et al.. (2009). Diversification of rice-based cropping system and their impact on energy utilization and system production.. Journal of Crop and Weed. 5(1). 167–170. 6 indexed citations

16.

Majumdar, Kaushik, et al.. (2009). Influence of mineralogy on potassium availability in Indian soils.. 5(11). 49–64. 4 indexed citations

17.

Chatterjee, Snigdhansu & Samarjit Das. (2003). Parameter Estimation in Conditional Heteroscedastic Models. Communication in Statistics- Theory and Methods. 32(6). 1135–1153. 4 indexed citations

18.

Bose, Arup & Snigdhansu Chatterjee. (2002). COMPARISON OF BOOTSTRAP AND JACKKNIFE VARIANCE ESTIMATORS IN LINEAR REGRESSION: SECOND ORDER RESULTS. Statistica Sinica. 12(2). 575–598. 5 indexed citations

19.

Chatterjee, Snigdhansu & Arup Bose. (2000). VARIANCE ESTIMATION IN HIGH DIMENSIONAL REGRESSION MODELS. Statistica Sinica. 10(2). 497–515. 8 indexed citations

20.

Nayak, D.C., et al.. (1999). Studies on Pedogenesis in a Soil Chronosequence in West Bengal. Journal of the Indian Society of Soil Science. 47(2). 322–328. 2 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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