V. Susarla

1.4k total citations
44 papers, 934 citations indexed

About

V. Susarla is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, V. Susarla has authored 44 papers receiving a total of 934 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Statistics and Probability, 25 papers in Artificial Intelligence and 4 papers in Management Science and Operations Research. Recurrent topics in V. Susarla's work include Statistical Methods and Inference (39 papers), Bayesian Methods and Mixture Models (25 papers) and Statistical Methods and Bayesian Inference (12 papers). V. Susarla is often cited by papers focused on Statistical Methods and Inference (39 papers), Bayesian Methods and Mixture Models (25 papers) and Statistical Methods and Bayesian Inference (12 papers). V. Susarla collaborates with scholars based in United States and Canada. V. Susarla's co-authors include John Van Ryzin, Hira L. Koul, J. R. Blum, Joseph C. Gardiner, G.G. Walter, Eswar G. Phadia, Anton Schίck, Wei‐Yann Tsai, J. K. Ghorai and Richard A. Johnson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Biometrika.

In The Last Decade

V. Susarla

44 papers receiving 826 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
V. Susarla United States	14	851	377	108	81	46	44	934
Constance van Eeden Canada	18	594 0.7×	194 0.5×	111 1.0×	100 1.2×	28 0.6×	70	816
Albert Y. Lo United States	13	738 0.9×	770 2.0×	47 0.4×	49 0.6×	35 0.8×	24	973
Jack C. Lee Taiwan	14	422 0.5×	272 0.7×	53 0.5×	68 0.8×	97 2.1×	24	703
M. V. Johns United States	14	432 0.5×	125 0.3×	195 1.8×	124 1.5×	19 0.4×	17	672
Teresa Ledwina Poland	15	705 0.8×	212 0.6×	123 1.1×	77 1.0×	54 1.2×	52	873
Saul Blumenthal United States	18	563 0.7×	153 0.4×	147 1.4×	138 1.7×	24 0.5×	48	717
H. W. Peers United Kingdom	7	421 0.5×	125 0.3×	83 0.8×	41 0.5×	29 0.6×	11	489
Jiunn Tzon Hwang United States	14	598 0.7×	182 0.5×	135 1.3×	118 1.5×	31 0.7×	33	739
N. Rao Chaganty United States	13	451 0.5×	128 0.3×	27 0.3×	96 1.2×	67 1.5×	46	739

Countries citing papers authored by V. Susarla

Since Specialization

Citations

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

Fields of papers citing papers by V. Susarla

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of V. Susarla

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Schίck, Anton & V. Susarla. (1990). An infinite dimensional convolution theorem with applications to random censoring and missing data models. Journal of Statistical Planning and Inference. 24(1). 13–23. 5 indexed citations

Gardiner, Joseph C. & V. Susarla. (1989). The performance of a sequential procedure for the estimation of the median survival time with specified accuracy. Sequential Analysis. 8(2). 191–204. 3 indexed citations

Schίck, Anton, et al.. (1988). EFFICIENT ESTIMATION OF FUNCTIONALS WITH CENSORED DATA. Statistics & Risk Modeling. 6(4). 8 indexed citations

Gardiner, Joseph C., V. Susarla, & John Van Ryzin. (1988). On bounded length confidence intervals for quantiles of the survival distribution. Journal of Statistical Planning and Inference. 18(2). 245–253. 2 indexed citations

Klotz, Jerome, et al.. (1986). Small sample relative performance of the spline smooth survival estimator. Communications in Statistics - Simulation and Computation. 15(3). 271–298. 3 indexed citations

Gardiner, Joseph C., V. Susarla, & John Van Ryzin. (1986). Time Sequential Estimation of the Exponential Mean Under Random Withdrawals. The Annals of Statistics. 14(2). 15 indexed citations

Gardiner, Joseph C. & V. Susarla. (1984). Risk-efficient estimation of the mean exponential survival time under random censoring.. Proceedings of the National Academy of Sciences. 81(18). 5906–5909. 10 indexed citations

Phadia, Eswar G. & V. Susarla. (1983). Nonparametric bayesian estimation of a survival curve with dependent censoring mechanism. Annals of the Institute of Statistical Mathematics. 35(3). 389–400. 7 indexed citations

Susarla, V. & John Van Ryzin. (1980). Large Sample Theory for an Estimator of the Mean Survival Time from Censored Samples. The Annals of Statistics. 8(5). 53 indexed citations

10.

Koul, Hira L. & V. Susarla. (1980). Testing for New Better than Used in Expectation with Incomplete Data. Journal of the American Statistical Association. 75(372). 952–956. 22 indexed citations

11.

Susarla, V., et al.. (1980). Shrinkage estimation in nonparametric Bayesian survival analysis: a simulation study. Communications in Statistics - Simulation and Computation. 9(3). 271–298. 5 indexed citations

12.

Susarla, V. & John Van Ryzin. (1980). Addendum to "Large Sample Theory for a Bayesian Nonparametric Survival Curve Estimator Based on Censored Data". The Annals of Statistics. 8(3). 2 indexed citations

13.

Phadia, Eswar G. & V. Susarla. (1979). An emprical bayes approach to two-sample problems with censored data. Communication in Statistics- Theory and Methods. 8(13). 1327–1351. 8 indexed citations

14.

Johnson, Richard A., V. Susarla, & John Van Ryzin. (1979). Bayesian non-parametric estimation for age-dependent branching processes. Stochastic Processes and their Applications. 9(3). 307–318. 13 indexed citations

15.

Susarla, V., et al.. (1979). Empirical bayes interval estimates involving uniform distributions. Communication in Statistics- Theory and Methods. 8(4). 385–397. 3 indexed citations

16.

Susarla, V., et al.. (1979). Shrinkage Estimation in Nonparametric Bayesian Survival Analysis. 2 indexed citations

17.

Susarla, V. & John Van Ryzin. (1978). Large Sample Theory for a Bayesian Nonparametric Survival Curve Estimator Based on Censored Samples. The Annals of Statistics. 6(4). 32 indexed citations

18.

Blum, J. R. & V. Susarla. (1977). On the posterior distribution of a dirichlet process given randomly right censored observations. Stochastic Processes and their Applications. 5(3). 207–211. 21 indexed citations

19.

Susarla, V., et al.. (1976). Rates in the empirical bayes estimation problem with non-identical components. Annals of the Institute of Statistical Mathematics. 28(1). 389–397. 8 indexed citations

20.

Susarla, V., et al.. (1975). An empirical bayes two action problem with nonidentical components for a translated exponential distribution. Communications in Statistics. 4(8). 767–775. 6 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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