Prabir Burman

1.7k total citations
37 papers, 1.2k citations indexed

About

Prabir Burman is a scholar working on Statistics and Probability, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Prabir Burman has authored 37 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Statistics and Probability, 8 papers in Artificial Intelligence and 7 papers in Control and Systems Engineering. Recurrent topics in Prabir Burman's work include Advanced Statistical Methods and Models (9 papers), Statistical Methods and Inference (6 papers) and Control Systems and Identification (5 papers). Prabir Burman is often cited by papers focused on Advanced Statistical Methods and Models (9 papers), Statistical Methods and Inference (6 papers) and Control Systems and Identification (5 papers). Prabir Burman collaborates with scholars based in United States, India and Canada. Prabir Burman's co-authors include Edmond Chow, Deborah Nolan, Melissa M. Grigione, Wolfgang Polonik, Malay Ghosal, K. E. Savage, Andrew D. Richardson, Robert S. Evans, Lawrence B. Flanagan and S. P. Urbanski and has published in prestigious journals such as Journal of the American Statistical Association, Biometrika and Biological Conservation.

In The Last Decade

Prabir Burman

33 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Prabir Burman United States	16	226	226	138	125	114	37	1.2k
Charles L. Dunn United States	7	137 0.6×	137 0.6×	64 0.5×	74 0.6×	90 0.8×	22	1.4k
Fabian Scheipl Germany	19	198 0.9×	497 2.2×	108 0.8×	119 1.0×	87 0.8×	43	1.8k
Kathleen Kocherlakota Canada	9	278 1.2×	398 1.8×	88 0.6×	89 0.7×	82 0.7×	23	1.5k
J.S. Urban Hjorth United States	6	147 0.7×	176 0.8×	81 0.6×	57 0.5×	50 0.4×	8	1.1k
Allan McQuarrie United States	9	177 0.8×	300 1.3×	77 0.6×	58 0.5×	49 0.4×	13	1.1k
In-Kwon Yeo South Korea	7	181 0.8×	161 0.7×	133 1.0×	48 0.4×	34 0.3×	23	1.3k
Zhi Geng China	20	346 1.5×	127 0.6×	123 0.9×	84 0.7×	108 0.9×	85	1.4k
G. Tunnicliffe Wilson United Kingdom	18	212 0.9×	248 1.1×	86 0.6×	94 0.8×	338 3.0×	38	1.6k
Natalie Neumeyer Germany	17	127 0.6×	551 2.4×	64 0.5×	59 0.5×	92 0.8×	43	1.4k
Gordon J. Ross United Kingdom	11	486 2.2×	198 0.9×	137 1.0×	73 0.6×	34 0.3×	25	1.6k

Countries citing papers authored by Prabir Burman

Since Specialization

Citations

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

Fields of papers citing papers by Prabir Burman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prabir Burman

This figure shows the co-authorship network connecting the top 25 collaborators of Prabir Burman. A scholar is included among the top collaborators of Prabir Burman 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 Prabir Burman. Prabir Burman 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

Burman, Prabir, et al.. (2018). Local Linear Estimation for Spatial Random Processes with Stochastic Trend and Stationary Noise. Sankhya B. 80(2). 369–394.

Burman, Prabir, et al.. (2018). Can We Do More With Less While Building Predictive Models? A Study in Parsimony of Risk Models for Predicting Heart Failure Readmissions. CIN Computers Informatics Nursing. 37(6). 306–314. 2 indexed citations

Burman, Prabir, et al.. (2016). Analyzing 30-Day Readmission Rate for Heart Failure Using Different Predictive Models. Studies in health technology and informatics. 225. 143–7. 10 indexed citations

Burman, Prabir. (2015). A Hilbert-type inequality. Mathematical Inequalities & Applications. 1253–1260.

Ghosal, Malay, Prabir Burman, Vineeta Singh, et al.. (2014). Correlates of Functional Outcome among Stroke Survivors in a Developing Country–A Prospective Community-based Study from India. Journal of Stroke and Cerebrovascular Diseases. 23(10). 2614–2621. 12 indexed citations

Bhowmick, Kaushik, et al.. (2013). Assessment of Insulin Resistance and Metabolic Syndrome in Drug Naive Patients of Bipolar Disorder. Indian Journal of Clinical Biochemistry. 29(1). 51–56. 35 indexed citations

Das, Sujata, Avijit Hazra, Malay Ghosal, et al.. (2012). Cognitive Dysfunction in Stroke Survivors: A Community-Based Prospective Study from Kolkata, India. Journal of Stroke and Cerebrovascular Diseases. 22(8). 1233–1242. 25 indexed citations

Burman, Prabir & Wolfgang Polonik. (2008). Multivariate mode hunting: Data analytic tools with measures of significance. Journal of Multivariate Analysis. 100(6). 1198–1218. 22 indexed citations

Burman, Prabir. (2006). Sharp bounds for singular values of fractional integral operators. Journal of Mathematical Analysis and Applications. 327(1). 251–256.

10.

Burman, Prabir. (2006). Approximate singular values of the fractional difference and summation operators. Linear Algebra and its Applications. 416(2-3). 677–687. 1 indexed citations

11.

Burman, Prabir & Robert H. Shumway. (2006). Generalized Exponential Predictors for Time Series Forecasting. Journal of the American Statistical Association. 101(476). 1598–1606. 8 indexed citations

12.

Richardson, Andrew D., B. H. Braswell, David Y. Hollinger, et al.. (2006). Comparing simple respiration models for eddy flux and dynamic chamber data. Agricultural and Forest Meteorology. 141(2-4). 219–234. 110 indexed citations

13.

Burman, Prabir. (2003). On some testing problems for sparse contingency tables. Journal of Multivariate Analysis. 88(1). 1–18. 5 indexed citations

14.

Burman, Prabir. (2000). What is Revealed in a Mountain Lion's Heel: Using Heel Shape to Ascertain Identity. 36. 21–26. 1 indexed citations

15.

Grigione, Melissa M., Prabir Burman, Vernon C. Bleich, & Becky M. Pierce. (1999). Identifying individual mountain lions Felis concolor by their tracks: refinement of an innovative technique. Biological Conservation. 88(1). 25–32. 38 indexed citations

16.

Burman, Prabir, Edmond Chow, & Deborah Nolan. (1994). A cross-validatory method for dependent data. Biometrika. 81(2). 351–358. 130 indexed citations

17.

Burman, Prabir, Edmond Chow, & Deborah Nolan. (1994). A Cross-Validatory Method for Dependent Data. Biometrika. 81(2). 351–351. 10 indexed citations

18.

Burman, Prabir. (1991). Regression function estimation from dependent observations. Journal of Multivariate Analysis. 36(2). 263–279. 19 indexed citations

19.

Burman, Prabir. (1990). Estimation of generalized additive models. Journal of Multivariate Analysis. 32(2). 230–255. 16 indexed citations

20.

Burman, Prabir, et al.. (1989). Nonparametric Estimation of a Regression Function. The Annals of Statistics. 17(4). 20 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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