Vivekananda Roy

446 total citations
30 papers, 224 citations indexed

About

Vivekananda Roy is a scholar working on Statistics and Probability, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Vivekananda Roy has authored 30 papers receiving a total of 224 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Statistics and Probability, 15 papers in Artificial Intelligence and 2 papers in Molecular Biology. Recurrent topics in Vivekananda Roy's work include Statistical Methods and Inference (19 papers), Statistical Methods and Bayesian Inference (15 papers) and Markov Chains and Monte Carlo Methods (15 papers). Vivekananda Roy is often cited by papers focused on Statistical Methods and Inference (19 papers), Statistical Methods and Bayesian Inference (15 papers) and Markov Chains and Monte Carlo Methods (15 papers). Vivekananda Roy collaborates with scholars based in United States, United Kingdom and France. Vivekananda Roy's co-authors include James P. Hobert, Xin Wang, Krishna B. Athreya, Dipak K. Dey, Sounak Chakraborty, Evangelos Evangelou, Christian P. Robert, Zhengyuan Zhu, Somak Dutta and Jarad Niemi and has published in prestigious journals such as PLANT PHYSIOLOGY, Biometrics and Journal of the Royal Statistical Society Series B (Statistical Methodology).

In The Last Decade

Vivekananda Roy

29 papers receiving 216 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vivekananda Roy United States 10 181 111 15 13 11 30 224
Silvelyn Zwanzig Sweden 7 65 0.4× 16 0.1× 4 0.3× 11 0.8× 9 0.8× 40 131
M. N. Murthy India 5 303 1.7× 69 0.6× 2 0.1× 6 0.5× 7 0.6× 15 373
Oscar Hernán Madrid Padilla United States 8 47 0.3× 34 0.3× 1 0.1× 7 0.5× 4 0.4× 22 115
Sanying Feng China 11 237 1.3× 36 0.3× 74 5.7× 4 0.4× 36 290
Arun Kumar Kuchibhotla United States 7 103 0.6× 57 0.5× 5 0.4× 1 0.1× 27 175
Mohamed Chaouch France 6 64 0.4× 32 0.3× 1 0.1× 8 0.6× 4 0.4× 11 95
Nursel Koyuncu Türkiye 13 569 3.1× 55 0.5× 2 0.1× 2 0.2× 4 0.4× 48 604
Kofi P. Adragni United States 6 55 0.3× 35 0.3× 4 0.3× 10 100
A. H. El-Bassiouny Egypt 10 249 1.4× 44 0.4× 2 0.1× 4 0.3× 1 0.1× 23 289

Countries citing papers authored by Vivekananda Roy

Since Specialization
Citations

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

Fields of papers citing papers by Vivekananda Roy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vivekananda Roy

This figure shows the co-authorship network connecting the top 25 collaborators of Vivekananda Roy. A scholar is included among the top collaborators of Vivekananda Roy 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 Vivekananda Roy. Vivekananda Roy 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.
Dutta, Somak, et al.. (2023). The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture. PLANT PHYSIOLOGY. 192(3). 2394–2403. 11 indexed citations
2.
Roy, Vivekananda & Lijin Zhang. (2022). Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs. Journal of Computational and Graphical Statistics. 32(2). 501–512. 1 indexed citations
3.
Li, Dongjin, Somak Dutta, & Vivekananda Roy. (2022). Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings. Journal of Computational and Graphical Statistics. 32(1). 61–73. 4 indexed citations
4.
Roy, Vivekananda & Evangelos Evangelou. (2022). Selection of Proposal Distributions for Multiple Importance Sampling. Statistica Sinica. 1 indexed citations
5.
Roy, Vivekananda, et al.. (2021). Block Gibbs samplers for logistic mixed models: Convergence properties and a comparison with full Gibbs samplers. Electronic Journal of Statistics. 15(2).
6.
Wang, Run, Somak Dutta, & Vivekananda Roy. (2020). A note on marginal correlation based screening. Statistical Analysis and Data Mining The ASA Data Science Journal. 14(1). 88–92. 1 indexed citations
7.
Evangelou, Evangelos & Vivekananda Roy. (2019). Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling. Spatial Statistics. 29. 289–315. 4 indexed citations
8.
Wang, Xin & Vivekananda Roy. (2018). Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors. Electronic Journal of Statistics. 12(2). 5 indexed citations
9.
Wang, Xin & Vivekananda Roy. (2018). Analysis of the Pólya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models. Statistics & Probability Letters. 137. 251–256. 5 indexed citations
10.
Wang, Xin, Vivekananda Roy, & Zhengyuan Zhu. (2017). A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach. Statistics and Computing. 28(5). 1083–1094. 2 indexed citations
11.
Roy, Vivekananda, Evangelos Evangelou, & Zhengyuan Zhu. (2015). Efficient Estimation and Prediction for the Bayesian Binary Spatial Model with Flexible Link Functions. Biometrics. 72(1). 289–298. 10 indexed citations
12.
Athreya, Krishna B. & Vivekananda Roy. (2015). Estimation of integrals with respect to infinite measures using regenerative sequences. Journal of Applied Probability. 52(4). 1133–1145. 1 indexed citations
13.
Athreya, Krishna B. & Vivekananda Roy. (2015). Estimation of integrals with respect to infinite measures using regenerative sequences. Journal of Applied Probability. 52(4). 1133–1145. 2 indexed citations
14.
Niemi, Jarad, et al.. (2015). Interweaving Markov Chain Monte Carlo Strategies for Efficient Estimation of Dynamic Linear Models. Journal of Computational and Graphical Statistics. 26(1). 152–159. 5 indexed citations
15.
Roy, Vivekananda & Mark S. Kaiser. (2013). Posterior propriety for Bayesian binomial regression models with a parametric family of link functions. Statistical Methodology. 13. 25–41. 1 indexed citations
16.
Athreya, Krishna B. & Vivekananda Roy. (2013). When is a Markov chain regenerative?. Statistics & Probability Letters. 84. 22–26. 1 indexed citations
17.
Roy, Vivekananda. (2013). Efficient estimation of the link function parameter in a robust Bayesian binary regression model. Computational Statistics & Data Analysis. 73. 87–102. 9 indexed citations
18.
Roy, Vivekananda. (2012). Convergence rates for MCMC algorithms for a robust Bayesian binary regression model. Electronic Journal of Statistics. 6(none). 12 indexed citations
19.
Roy, Vivekananda. (2011). Spectral analytic comparisons for data augmentation. Statistics & Probability Letters. 82(1). 103–108. 6 indexed citations
20.
Roy, Vivekananda & James P. Hobert. (2010). On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors. Journal of Multivariate Analysis. 101(5). 1190–1202. 16 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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