Pinyuen Chen

467 total citations
48 papers, 301 citations indexed

About

Pinyuen Chen is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, Pinyuen Chen has authored 48 papers receiving a total of 301 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistics and Probability, 20 papers in Management Science and Operations Research and 12 papers in Artificial Intelligence. Recurrent topics in Pinyuen Chen's work include Optimal Experimental Design Methods (16 papers), Statistical Methods in Clinical Trials (15 papers) and Advanced Statistical Methods and Models (13 papers). Pinyuen Chen is often cited by papers focused on Optimal Experimental Design Methods (16 papers), Statistical Methods in Clinical Trials (15 papers) and Advanced Statistical Methods and Models (13 papers). Pinyuen Chen collaborates with scholars based in United States, Taiwan and Japan. Pinyuen Chen's co-authors include Michael C. Wicks, William L. Melvin, Nitis Mukhopadhyay, S. Panchapakesan, Lingling Wu, Tiee‐Jian Wu, Yanjun Yan, Robert E. Bechhofer, Makoto Aoshima and Milton Sobel and has published in prestigious journals such as Journal of the American Statistical Association, IEEE Transactions on Signal Processing and Signal Processing.

In The Last Decade

Pinyuen Chen

43 papers receiving 293 citations

Peers

Countries citing papers authored by Pinyuen Chen

Since Specialization

Citations

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

Fields of papers citing papers by Pinyuen Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pinyuen Chen

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

#	Title	Journal	Authors	Indexed citations
1	A Curtailed Procedure for Selecting Among Treatments With Two Bernoulli Endpoints	Sankhya B	Pinyuen Chen et al.	2
2	A Two-Stage Design for Comparative Clinical Trials: the Heteroscedastic Solution	Sankhya B	Pinyuen Chen et al.	1
3	The weighted average information criterion for multivariate regression model selection	Signal Processing	Tiee‐Jian Wu, Pinyuen Chen et al.	10
4	A Hybrid Selection and Testing Procedure with Curtailment for Comparative Clinical Trials	Sequential Analysis	Pinyuen Chen et al.	4
5	Motivation for using search engines: A two‐factor model	Journal of the American Society for Information Science and Technology	Lingling Wu, Pinyuen Chen et al.	18
6	On Selecting Among Treatments with Binomial Outcomes	Communication in Statistics- Theory and Methods	Pinyuen Chen et al.	7
7	A confidence interval for the number of principal components	Journal of Statistical Planning and Inference	Pinyuen Chen	1
8	Sequential Procedures for Selecting the Most Probable Multinomial Cell When a Nuisance Cell Is Present	Communication in Statistics- Theory and Methods	Makoto Aoshima, Pinyuen Chen et al.	4
9	A Two-Stage Selection and Testing Design for Comparing Several Normal Means with a Standard: The Case of Common, Known Variance	Sequential Analysis	Linda M. Rollin, Pinyuen Chen	0
10	Development of a lower confidence limit for the number of signals	IEEE Transactions on Signal Processing	Pinyuen Chen, Michael C. Wicks	4
11	An integrated selection formulation for the best normal mean: theunequal and unknown variance case	Journal of Applied Mathematics and Decision Sciences	Pinyuen Chen et al.	1
12	A procedure for detecting the number of signal components in a radar measurement		Pinyuen Chen, Michael C. Wicks	6
13	A two-stage procedure for selecting the largest multinomial cell probability when nuisance cell is present	Sequential Analysis	Makoto Aoshima, Pinyuen Chen	2
14	Screening among Multivariate Normal Data	Journal of Multivariate Analysis	Pinyuen Chen, William L. Melvin et al.	88
15	Advances in Statistical Decision Theory and Applications	Journal of the American Statistical Association	Pinyuen Chen, S. Panchapakesan et al.	12
16	Selecting among the multinomial losers	Sequential Analysis	Pinyuen Chen, S. Panchapakesan et al.	2
17	A Two‐stage Design for Comparing Clinical Trials	Biometrical Journal	Pinyuen Chen et al.	2
18	Closed inverse sampling procedure for selecting the largest multinomial cell probability	Communications in Statistics - Simulation and Computation	Pinyuen Chen	5
19	On the least favorable configuration in mult1hgmial selection problems	Communication in Statistics- Theory and Methods	Pinyuen Chen	8
20	Subset selection for the least probable multinomial cell	Annals of the Institute of Statistical Mathematics	Pinyuen Chen	3

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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