Fengchun Peng

428 total citations
7 papers, 294 citations indexed

About

Fengchun Peng is a scholar working on Artificial Intelligence, Statistics and Probability and Signal Processing. According to data from OpenAlex, Fengchun Peng has authored 7 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 4 papers in Statistics and Probability and 2 papers in Signal Processing. Recurrent topics in Fengchun Peng's work include Bayesian Methods and Mixture Models (4 papers), Statistical Methods and Bayesian Inference (3 papers) and Advanced Statistical Methods and Models (2 papers). Fengchun Peng is often cited by papers focused on Bayesian Methods and Mixture Models (4 papers), Statistical Methods and Bayesian Inference (3 papers) and Advanced Statistical Methods and Models (2 papers). Fengchun Peng collaborates with scholars based in United States. Fengchun Peng's co-authors include Dipak K. Dey, Martin A. Tanner, Robert A. Jacobs and Alan E. Gelfand and has published in prestigious journals such as Journal of the American Statistical Association, Neural Networks and Medical Decision Making.

In The Last Decade

Fengchun Peng

7 papers receiving 273 citations

Peers

Fengchun Peng

SP

AI

FINAN

SPU

MSOR

Christophe Crambes France

Yu. S. Kharin Belarus

Alexander R. de Leon Canada

Luis E. Nieto‐Barajas Mexico

Alexandre G. Patriota Brazil

H. Papageorgiou Greece

Weihua Zhao China

Clare A. McGrory Australia

Hammou El Barmi United States

Marian Hristache France

Christophe Crambes France

Fengchun Peng

224 ×1.1

SP

84 ×0.5

AI

17 ×1.0

FINAN

24 ×1.5

SPU

12 ×0.8

MSOR

Citations per year, relative to Fengchun Peng Fengchun Peng (= 1×) peers Christophe Crambes

Countries citing papers authored by Fengchun Peng

Since Specialization

Citations

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

Fields of papers citing papers by Fengchun Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fengchun Peng

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

All Works

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7 of 7 papers shown

1.

Jacobs, Robert A., Fengchun Peng, & Martin A. Tanner. (1997). A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures. Neural Networks. 10(2). 231–241. 42 indexed citations

2.

Dey, Dipak K., Alan E. Gelfand, & Fengchun Peng. (1997). Overdispersed generalized linear models. Journal of Statistical Planning and Inference. 64(1). 93–107. 30 indexed citations

3.

Peng, Fengchun, et al.. (1996). Bayesian Analysis of ROC Curves Using Markov-chain Monte Carlo Methods. Medical Decision Making. 16(4). 404–411. 17 indexed citations

4.

Peng, Fengchun, Robert A. Jacobs, & Martin A. Tanner. (1996). Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models with an Application to Speech Recognition. Journal of the American Statistical Association. 91(435). 953–960. 90 indexed citations

5.

Jacobs, Robert A., Martin A. Tanner, & Fengchun Peng. (1996). Bayesian inference for hierarchical mixtures-of-experts with applications to regression and classification. Statistical Methods in Medical Research. 5(4). 375–390. 3 indexed citations

6.

Peng, Fengchun, Robert A. Jacobs, & Martin A. Tanner. (1996). Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition. Journal of the American Statistical Association. 91(435). 953–953. 18 indexed citations

7.

Peng, Fengchun & Dipak K. Dey. (1995). Bayesian analysis of outlier problems using divergence measures. Canadian Journal of Statistics. 23(2). 199–213. 94 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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