Gemai Chen

2.1k total citations
66 papers, 1.7k citations indexed

About

Gemai Chen is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Management Science and Operations Research. According to data from OpenAlex, Gemai Chen has authored 66 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Statistics and Probability, 15 papers in Statistics, Probability and Uncertainty and 10 papers in Management Science and Operations Research. Recurrent topics in Gemai Chen's work include Advanced Statistical Methods and Models (34 papers), Statistical Methods and Inference (29 papers) and Statistical Methods and Bayesian Inference (18 papers). Gemai Chen is often cited by papers focused on Advanced Statistical Methods and Models (34 papers), Statistical Methods and Inference (29 papers) and Statistical Methods and Bayesian Inference (18 papers). Gemai Chen collaborates with scholars based in Canada, China and United States. Gemai Chen's co-authors include N. Balakrishnan, Smiley W. Cheng, Jinhong You, Hansheng Xie, Lingyun Zhang, Philippe Castagliola, Yong Zhou, Лей Ши, Richard Lockhart and Giovanni Celano and has published in prestigious journals such as Management Science, Limnology and Oceanography and The Annals of Statistics.

In The Last Decade

Gemai Chen

60 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gemai Chen Canada 21 1.2k 1.0k 218 194 128 66 1.7k
F. F. Gan Singapore 23 749 0.6× 1.3k 1.3× 175 0.8× 235 1.2× 26 0.2× 48 1.6k
Warren Gilchrist United Kingdom 10 286 0.2× 260 0.2× 156 0.7× 53 0.3× 53 0.4× 26 807
Marcelo Bourguignon Brazil 20 1.3k 1.1× 649 0.6× 156 0.7× 31 0.2× 304 2.4× 109 1.5k
Mahdi Doostparast Iran 15 640 0.6× 484 0.5× 77 0.4× 27 0.1× 59 0.5× 80 810
Alan M. Polansky United States 14 287 0.2× 274 0.3× 152 0.7× 80 0.4× 39 0.3× 41 686
Irving W. Burr United States 15 354 0.3× 388 0.4× 130 0.6× 54 0.3× 41 0.3× 25 733
Hassan S. Bakouch Egypt 19 1.4k 1.2× 700 0.7× 223 1.0× 52 0.3× 468 3.7× 154 1.7k
S. Ejaz Ahmed Canada 17 785 0.7× 134 0.1× 104 0.5× 46 0.2× 98 0.8× 159 1.1k
D. A. Evans United Kingdom 6 382 0.3× 659 0.6× 69 0.3× 110 0.6× 11 0.1× 10 842
J. C. Naylor United Kingdom 9 666 0.6× 347 0.3× 116 0.5× 27 0.1× 80 0.6× 11 979

Countries citing papers authored by Gemai Chen

Since Specialization
Citations

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

Fields of papers citing papers by Gemai Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gemai Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Gemai Chen. A scholar is included among the top collaborators of Gemai 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 Gemai Chen. Gemai Chen 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.
Chen, Gemai, et al.. (2019). Projection sparse principal component analysis: An efficient least squares method. Journal of Multivariate Analysis. 173. 366–382. 6 indexed citations
2.
Ши, Лей, Jun Lü, Jianhua Zhao, & Gemai Chen. (2015). Case deletion diagnostics for GMM estimation. Computational Statistics & Data Analysis. 95. 176–191. 3 indexed citations
3.
Chen, Gemai, et al.. (2015). A new hybrid estimation method for the generalized pareto distribution. Communication in Statistics- Theory and Methods. 45(14). 4285–4294. 5 indexed citations
4.
You, Jinhong, Yong Zhou, & Gemai Chen. (2013). Statistical inference for multivariate partially linear regression models. Canadian Journal of Statistics. 41(1). 1–22. 7 indexed citations
5.
Zhang, Lingyun, Xinzhong Xu, & Gemai Chen. (2012). The Exact Likelihood Ratio Test for Equality of Two Normal Populations. The American Statistician. 66(3). 180–184. 7 indexed citations
6.
Castagliola, Philippe, Giovanni Celano, & Gemai Chen. (2009). THE EXACT RUN LENGTH DISTRIBUTION AND DESIGN OF THE S2 CHART WHEN THE IN-CONTROL VARIANCE IS ESTIMATED. International Journal of Reliability Quality and Safety Engineering. 16(1). 23–38. 57 indexed citations
7.
Ши, Лей & Gemai Chen. (2008). Case deletion diagnostics in multilevel models. Journal of Multivariate Analysis. 99(9). 1860–1877. 11 indexed citations
8.
Ши, Лей & Gemai Chen. (2008). Detection of outliers in multilevel models. Journal of Statistical Planning and Inference. 138(10). 3189–3199. 17 indexed citations
9.
Liu, Feng, Gemai Chen, & Min Chen. (2008). Testing Serial Correlation in Partial Linear Errors-in-Variables Models Based on Empirical Likelihood. Communication in Statistics- Theory and Methods. 37(12). 1905–1918. 8 indexed citations
10.
You, Jinhong, Gemai Chen, & Yong Zhou. (2007). Statistical inference of partially linear regression models with heteroscedastic errors. Journal of Multivariate Analysis. 98(8). 1539–1557. 32 indexed citations
11.
You, Jinhong & Gemai Chen. (2007). On inference for a semiparametric partially linear regression model with serially correlated errors. Canadian Journal of Statistics. 35(4). 515–531. 3 indexed citations
12.
You, Jinhong & Gemai Chen. (2005). Semiparametric generalized least squares estimation in partially linear regression models with correlated errors. Journal of Statistical Planning and Inference. 137(1). 117–132. 35 indexed citations
13.
You, Jinhong & Gemai Chen. (2005). Estimation of a semiparametric varying-coefficient partially linear errors-in-variables model. Journal of Multivariate Analysis. 97(2). 324–341. 71 indexed citations
14.
You, Jinhong, Gemai Chen, & Xian Zhou. (2005). β -Spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series Errors. American Journal of Applied Sciences. 2(9). 1343–1349. 3 indexed citations
15.
Chen, Gemai. (2004). B-SPLINE ESTIMATION FOR PARTIALLY LINEAR REGRESSION MODELS WITH HETEROSCEDASTICITY. 1 indexed citations
16.
You, Jinhong, Xian Zhou, & Gemai Chen. (2003). Jackknifing in partially linear regression models with serially correlated errors. Journal of Multivariate Analysis. 92(2). 386–404. 6 indexed citations
17.
Sun, Xiaoqian, Jinhong You, Gemai Chen, & Xian Zhou. (2002). CONVERGENCE RATES OF ESTIMATORS IN PARTIAL LINEAR REGRESSION MODELS WITH MA(∞) ERROR PROCESS. Communication in Statistics- Theory and Methods. 31(12). 2251–2273. 16 indexed citations
18.
Zhang, Lingyun & Gemai Chen. (2002). A NOTE ON EWMA CHARTS FOR MONITORING MEAN CHANGES IN NORMAL PROCESSES. Communication in Statistics- Theory and Methods. 31(4). 649–661. 7 indexed citations
19.
Chen, Gemai. (1998). The run length distributions of the R, s and s2 control charts when is estimated. Canadian Journal of Statistics. 26(2). 311–322. 63 indexed citations
20.
Chen, Gemai & Smiley W. Cheng. (1998). MAX CHART: COMBINING X-BAR CHART AND S CHART. 70 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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