Kim-Hung Pho

718 total citations
27 papers, 487 citations indexed

About

Kim-Hung Pho is a scholar working on Statistics and Probability, Artificial Intelligence and Finance. According to data from OpenAlex, Kim-Hung Pho has authored 27 papers receiving a total of 487 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 7 papers in Artificial Intelligence and 5 papers in Finance. Recurrent topics in Kim-Hung Pho's work include Statistical Methods and Bayesian Inference (7 papers), Statistical Distribution Estimation and Applications (6 papers) and Bayesian Methods and Mixture Models (4 papers). Kim-Hung Pho is often cited by papers focused on Statistical Methods and Bayesian Inference (7 papers), Statistical Distribution Estimation and Applications (6 papers) and Bayesian Methods and Mixture Models (4 papers). Kim-Hung Pho collaborates with scholars based in Vietnam, Iran and Taiwan. Kim-Hung Pho's co-authors include Mohammad Reza Mahmoudi, Mohsen Maleki, Darren Wraith, Mohammad Heydari, Dumitru Bǎleanu, Zulkefli Mansor, Z. Avazzadeh, Wing‐Keung Wong, Han Woo Park and Iman Raeesi Vanani and has published in prestigious journals such as SHILAP Revista de lepidopterología, Chaos Solitons & Fractals and Applied Mathematical Modelling.

In The Last Decade

Kim-Hung Pho

24 papers receiving 478 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kim-Hung Pho Vietnam 11 189 129 116 77 67 27 487
Mohsen Maleki Iran 19 207 1.1× 400 3.1× 126 1.1× 305 4.0× 61 0.9× 57 1.0k
Carlos Martín-Barreiro Ecuador 13 61 0.3× 61 0.5× 46 0.4× 42 0.5× 58 0.9× 38 402
Tanujit Chakraborty India 10 216 1.1× 170 1.3× 126 1.1× 10 0.1× 75 1.1× 35 606
Mustapha Rachdi France 17 146 0.8× 291 2.3× 118 1.0× 571 7.4× 48 0.7× 80 862
George Tsaklidis Greece 12 48 0.3× 89 0.7× 40 0.3× 46 0.6× 58 0.9× 53 397
Mark Briers United Kingdom 14 120 0.6× 480 3.7× 22 0.2× 64 0.8× 22 0.3× 27 873
Hamsa Bastani United States 11 25 0.1× 107 0.8× 78 0.7× 46 0.6× 173 2.6× 28 440
Petrônio Cândido de Lima e Silva Brazil 12 203 1.1× 267 2.1× 141 1.2× 11 0.1× 320 4.8× 33 951
Ferdinánd Filip Slovakia 4 116 0.6× 116 0.9× 67 0.6× 11 0.1× 42 0.6× 15 340
Huda M. Alshanbari Saudi Arabia 15 22 0.1× 66 0.5× 54 0.5× 306 4.0× 145 2.2× 75 671

Countries citing papers authored by Kim-Hung Pho

Since Specialization
Citations

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

Fields of papers citing papers by Kim-Hung Pho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kim-Hung Pho. 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 Kim-Hung Pho. The network helps show where Kim-Hung Pho may publish in the future.

Co-authorship network of co-authors of Kim-Hung Pho

This figure shows the co-authorship network connecting the top 25 collaborators of Kim-Hung Pho. A scholar is included among the top collaborators of Kim-Hung Pho 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 Kim-Hung Pho. Kim-Hung Pho 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.
Pho, Kim-Hung. (2023). Zero-inflated probit Bernoulli model: a new model for binary data. Communications in Statistics - Simulation and Computation. 54(7). 2324–2344. 4 indexed citations
2.
Pho, Kim-Hung. (2023). Zero-inflated logit probit model: a novel model for binary data. Communication in Statistics- Theory and Methods. 53(18). 6580–6599.
3.
Pho, Kim-Hung, et al.. (2023). Pearson chi-squared and unweighted residual sum of square tests of fit for a probit model. Communications in Statistics - Simulation and Computation. 53(12). 5842–5857. 2 indexed citations
4.
Pho, Kim-Hung. (2022). Goodness of fit test for a zero-inflated Bernoulli regression model. Communications in Statistics - Simulation and Computation. 53(2). 756–771. 7 indexed citations
5.
Pho, Kim-Hung. (2022). Improvements of the Newton–Raphson method. Journal of Computational and Applied Mathematics. 408. 114106–114106. 21 indexed citations
6.
Pho, Kim-Hung, et al.. (2021). A multi-level consensus function clustering ensemble. Soft Computing. 25(21). 13147–13165. 8 indexed citations
7.
Pho, Kim-Hung, et al.. (2021). EVALUATING THE EFFICIENCY OF VIETNAM BANKS USING DATA ENVELOPMENT ANALYSIS. Annals of Financial Economics. 16(2). 2150010–2150010. 7 indexed citations
8.
Lee, Shen‐Ming, Kim-Hung Pho, & Chin‐Shang Li. (2021). Validation likelihood estimation method for a zero-inflated Bernoulli regression model with missing covariates. Journal of Statistical Planning and Inference. 214. 105–127. 7 indexed citations
9.
Maleki, Mohsen, Mohammad Reza Mahmoudi, Mohammad Heydari, & Kim-Hung Pho. (2020). Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models. Chaos Solitons & Fractals. 140. 110151–110151. 53 indexed citations
10.
Mahmoudi, Mohammad Reza, et al.. (2020). Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries. Chaos Solitons & Fractals. 140. 110230–110230. 65 indexed citations
11.
Maleki, Mohsen, Mohammad Reza Mahmoudi, Darren Wraith, & Kim-Hung Pho. (2020). Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Medicine and Infectious Disease. 37. 101742–101742. 156 indexed citations
12.
Pho, Kim-Hung, et al.. (2020). Numerical study of nonlinear 2D optimal control problems with multi-term variable-order fractional derivatives in the Atangana-Baleanu-Caputo sense. Chaos Solitons & Fractals. 134. 109695–109695. 13 indexed citations
13.
Mahmoudi, Mohammad Reza, et al.. (2020). A Bayesian Approach to Heavy-Tailed Finite Mixture Autoregressive Models. Symmetry. 12(6). 929–929. 4 indexed citations
14.
Parvïn, Hamïd, et al.. (2020). Deep neural network as deep feature learner. Journal of Intelligent & Fuzzy Systems. 39(1). 355–369. 6 indexed citations
15.
Mahmoudi, Mohammad Reza, et al.. (2020). Numerical function optimization by conditionalized PSO algorithm. Journal of Intelligent & Fuzzy Systems. 39(3). 3275–3295.
16.
Pho, Kim-Hung, et al.. (2020). Prediction for the processes with almost cyclostationary structure. Journal of Statistical Computation and Simulation. 90(15). 2814–2822. 1 indexed citations
17.
Pho, Kim-Hung, et al.. (2019). Determining Distribution for the Quotients of Dependent and Independent Random Variables by Using Copulas. Journal of risk and financial management. 12(1). 42–42. 10 indexed citations
18.
Pho, Kim-Hung, et al.. (2019). Determining Distribution for the Product of Random Variables by Using Copulas. Risks. 7(1). 23–23. 18 indexed citations
19.
Mahmoudi, Mohammad Reza, Mohammad Heydari, Z. Avazzadeh, & Kim-Hung Pho. (2019). Goodness of fit test for almost cyclostationary processes. Digital Signal Processing. 96. 102597–102597. 29 indexed citations
20.

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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