S. H. Ong

3.5k total citations · 2 hit papers
140 papers, 2.6k citations indexed

About

S. H. Ong is a scholar working on Statistics and Probability, Artificial Intelligence and Modeling and Simulation. According to data from OpenAlex, S. H. Ong has authored 140 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Statistics and Probability, 53 papers in Artificial Intelligence and 17 papers in Modeling and Simulation. Recurrent topics in S. H. Ong's work include Statistical Distribution Estimation and Applications (56 papers), Bayesian Methods and Mixture Models (46 papers) and Statistical Methods and Bayesian Inference (27 papers). S. H. Ong is often cited by papers focused on Statistical Distribution Estimation and Applications (56 papers), Bayesian Methods and Mixture Models (46 papers) and Statistical Methods and Bayesian Inference (27 papers). S. H. Ong collaborates with scholars based in Malaysia, India and Canada. S. H. Ong's co-authors include Ramakrishnan Mukundan, Raveendran Paramesran, Pew‐Thian Yap, Ramesh C. Gupta, Yap Bee Wah, Subir Das, H. M. Srivastava, Hossein Jafari, Anup Singh and Subrata Chakraborty and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

S. H. Ong

128 papers receiving 2.5k 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.

Image analysis by Tchebichef moments

2001 704 citations S. H. Ong et al. profile →
Image analysis by krawtchouk moments

2003 471 citations Raveendran Paramesran, S. H. Ong et al. profile →

Peers

S. H. Ong

CVPR

AI

SP

MT

SP

Cun‐Hui Zhang United States

Raymond H. Chan Hong Kong

Jonathan Eckstein United States

É. A. Nadaraya Georgia

Vladimir Spokoiny Germany

J. R. Kettenring United States

Bernard Delyon France

Katya Scheinberg United States

Alexandre B. Tsybakov France

Barry G. Quinn Australia

Cun‐Hui Zhang United States

S. H. Ong

598 ×0.5

CVPR

1.2k ×2.5

AI

2.9k ×6.3

SP

62 ×0.2

MT

265 ×1.4

SP

Citations per year, relative to S. H. Ong S. H. Ong (= 1×) peers Cun‐Hui Zhang

Countries citing papers authored by S. H. Ong

Since Specialization

Citations

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

Fields of papers citing papers by S. H. Ong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. H. Ong. 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 S. H. Ong. The network helps show where S. H. Ong may publish in the future.

Co-authorship network of co-authors of S. H. Ong

This figure shows the co-authorship network connecting the top 25 collaborators of S. H. Ong. A scholar is included among the top collaborators of S. H. Ong 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 S. H. Ong. S. H. Ong 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

1.

Ong, S. H., et al.. (2024). Fractional Poisson distribution: some properties and parameter estimation. Ricerche di Matematica. 75(1). 45–72.

2.

Ong, S. H., et al.. (2024). The Modelling of Auto Insurance Claim-Frequency Counts by the Inverse Trinomial Distribution. Journal of risk and financial management. 18(1). 7–7.

3.

Chakraborty, Subrata, et al.. (2023). An Extension of the Geometric Distribution with Properties and Applications. SHILAP Revista de lepidopterología. 52(3). 124–142.

4.

Ong, S. H., et al.. (2023). Efficient anisotropic scaling and translation invariants of Tchebichef moments using image normalization. Pattern Recognition Letters. 169. 8–16.

5.

Ong, S. H., et al.. (2023). A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data. SHILAP Revista de lepidopterología. 6(3). 942–955.

6.

Ratnavelu, Kurunathan, et al.. (2021). Synchronization of BAM Cohen--Grossberg FCNNs with mixed time delays. Iranian journal of fuzzy systems. 18(2). 159–173. 2 indexed citations

7.

Ratnavelu, Kurunathan, et al.. (2020). (2001-5675) Synchronization of BAM Cohen--Grossberg FCNNs with mixed time delays. Iranian journal of fuzzy systems. 2 indexed citations

8.

Ong, S. H., et al.. (2019). The non-central negative binomial distribution: Further properties and applications. Communication in Statistics- Theory and Methods. 50(2). 329–344. 3 indexed citations

9.

Liu, Youbo, et al.. (2019). A shape-based cutting and clustering algorithm for multiple change-point detection. Journal of Computational and Applied Mathematics. 369. 112623–112623. 6 indexed citations

10.

Gupta, Ramesh C., et al.. (2018). Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data. The International Journal of Biostatistics. 14(1). 11 indexed citations

11.

Mukerjee, Rahul & S. H. Ong. (2015). Variance and covariance inequalities for truncated joint normal distribution via monotone likelihood ratio and log-concavity. Journal of Multivariate Analysis. 139. 1–6. 3 indexed citations

12.

Ong, S. H., et al.. (2012). A Lagrangian Non Central Negative Binomial Distribution of the First Kind. Communication in Statistics- Theory and Methods. 42(3). 466–477. 1 indexed citations

13.

Zhang, Zhijie, Dongmei Chen, Wenbao Liu, et al.. (2011). Nonparametric Evaluation of Dynamic Disease Risk: A Spatio-Temporal Kernel Approach. PLoS ONE. 6(3). e17381–e17381. 38 indexed citations

14.

Khang, Tsung Fei & S. H. Ong. (2007). A New Generalization of the Logarithmic Distribution Arising from the Inverse Trinomial Distribution. Communication in Statistics- Theory and Methods. 36(1). 3–21. 15 indexed citations

15.

Gupta, Ramesh C. & S. H. Ong. (2005). Analysis of Long-Tailed Count Data by Poisson Mixtures. Communication in Statistics- Theory and Methods. 34(3). 557–573. 34 indexed citations

16.

Ong, S. H., et al.. (1999). Probabilistic derivation of some generating functions for the Laguerre polynomials. Computers & Mathematics with Applications. 38(7-8). 21–29. 2 indexed citations

17.

Ong, S. H., et al.. (1997). Discriminant analysis of normal and malignant breast tissue based upon INAA investigation of elemental concentration. Applied Radiation and Isotopes. 48(1). 105–109. 13 indexed citations

18.

Ong, S. H.. (1993). Canonical expansions, correlation structure, and conditional distributions of bivariate distributions generated by mixtures. Communication in Statistics- Theory and Methods. 22(9). 163–168. 3 indexed citations

19.

Ong, S. H., et al.. (1986). On a generalized non-central negative binomial distribution. Communication in Statistics- Theory and Methods. 15(3). 1065–1079. 18 indexed citations

20.

Huang, R. L., et al.. (1968). Induced decomposition of t-butyl peroxide in solution. Chemical Communications (London). 1251–1251. 1 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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