Ming−Yen Cheng

1.5k total citations
52 papers, 951 citations indexed

About

Ming−Yen Cheng is a scholar working on Statistics and Probability, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Ming−Yen Cheng has authored 52 papers receiving a total of 951 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Statistics and Probability, 13 papers in Artificial Intelligence and 9 papers in Control and Systems Engineering. Recurrent topics in Ming−Yen Cheng's work include Statistical Methods and Inference (35 papers), Advanced Statistical Methods and Models (17 papers) and Bayesian Methods and Mixture Models (11 papers). Ming−Yen Cheng is often cited by papers focused on Statistical Methods and Inference (35 papers), Advanced Statistical Methods and Models (17 papers) and Bayesian Methods and Mixture Models (11 papers). Ming−Yen Cheng collaborates with scholars based in Taiwan, United States and Hong Kong. Ming−Yen Cheng's co-authors include Jianqing Fan, J. S. Marron, Peter Hall, Hau‐Tieng Wu, Liang Peng, Yu‐Chun Chen, Toshio Honda, Théo Gasser, Jin‐Ting Zhang and Wenyang Zhang and has published in prestigious journals such as Journal of the American Statistical Association, Biophysical Journal and Biometrika.

In The Last Decade

Ming−Yen Cheng

49 papers receiving 889 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming−Yen Cheng Taiwan	17	520	215	115	112	82	52	951
Marlene Müller Germany	10	375 0.7×	206 1.0×	79 0.7×	191 1.7×	59 0.7×	17	984
Joan G. Staniswalis United States	17	716 1.4×	195 0.9×	83 0.7×	75 0.7×	59 0.7×	37	1.1k
Hannes Leeb Austria	10	514 1.0×	137 0.6×	56 0.5×	208 1.9×	79 1.0×	29	900
J. Freeman United States	10	171 0.3×	239 1.1×	79 0.7×	61 0.5×	102 1.2×	28	1.1k
Mohsen Pourahmadi United States	17	524 1.0×	247 1.1×	102 0.9×	177 1.6×	55 0.7×	64	1.3k
Hans‐Georg Müller United States	13	747 1.4×	275 1.3×	148 1.3×	70 0.6×	68 0.8×	33	1.2k
Jan Mielniczuk Poland	17	437 0.8×	269 1.3×	66 0.6×	143 1.3×	42 0.5×	58	885
Tomaz J. Kozubowski United States	2	406 0.8×	278 1.3×	37 0.3×	151 1.3×	127 1.5×	2	1.0k
Lan Wang United States	19	1.0k 1.9×	337 1.6×	86 0.7×	140 1.3×	70 0.9×	60	1.5k
Jianhua Z. Huang United States	18	607 1.2×	378 1.8×	108 0.9×	125 1.1×	69 0.8×	39	1.4k

Countries citing papers authored by Ming−Yen Cheng

Since Specialization

Citations

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

Fields of papers citing papers by Ming−Yen Cheng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming−Yen Cheng. 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 Ming−Yen Cheng. The network helps show where Ming−Yen Cheng may publish in the future.

Co-authorship network of co-authors of Ming−Yen Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Ming−Yen Cheng. A scholar is included among the top collaborators of Ming−Yen Cheng 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 Ming−Yen Cheng. Ming−Yen Cheng 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

Jiang, Binyan, Jing Lv, Jialiang Li, & Ming−Yen Cheng. (2024). Robust model averaging prediction of longitudinal response with ultrahigh-dimensional covariates. Journal of the Royal Statistical Society Series B (Statistical Methodology). 87(2). 337–361. 1 indexed citations

Wang, Chenyu, Tsung‐Han Hsieh, Ming−Yen Cheng, et al.. (2022). FKBP51 mediates resilience to inflammation-induced anxiety through regulation of glutamic acid decarboxylase 65 expression in mouse hippocampus. Journal of Neuroinflammation. 19(1). 152–152. 19 indexed citations

Huang, Tao, et al.. (2021). Robust Inference for Nonstationary Time Series with Possibly Multiple Changing Periodic Structures. Journal of Business and Economic Statistics. 40(4). 1718–1731.

Cheng, Ming−Yen & Jianqing Fan. (2016). Peter Hall’s contributions to nonparametric function estimation and modeling. The Annals of Statistics. 44(5). 6 indexed citations

Cheng, Ming−Yen, Toshio Honda, & Jin‐Ting Zhang. (2015). Forward Variable Selection for Sparse Ultra-High Dimensional Varying Coefficient Models. Journal of the American Statistical Association. 111(515). 1209–1221. 33 indexed citations

Chen, Yu‐Chun, Ming−Yen Cheng, & Hau‐Tieng Wu. (2014). Non‐parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors. Journal of the Royal Statistical Society Series A (Statistics in Society). 1 indexed citations

Cheng, Ming−Yen, et al.. (2009). Confidence intervals for the first crossing point of two hazard functions. Lifetime Data Analysis. 15(4). 441–454. 10 indexed citations

Cheng, Ming−Yen & Marc Raimondo. (2008). Kernel Methods for Optimal Change-Points Estimation in Derivatives. Journal of Computational and Graphical Statistics. 17(1). 56–75. 15 indexed citations

Cheng, Ming−Yen, et al.. (2008). Conditional variance estimation in heteroscedastic regression models. Journal of Statistical Planning and Inference. 139(2). 236–245. 27 indexed citations

10.

Cheng, Ming−Yen & Liang Peng. (2007). Variance Reduction in Multiparameter Likelihood Models. Journal of the American Statistical Association. 102(477). 293–304. 6 indexed citations

11.

Cheng, Ming−Yen, et al.. (2006). Bandwidth Selection for Kernel Quantile Estimation. 44(3). 271–295. 10 indexed citations

12.

Cheng, Ming−Yen & Peter Hall. (2006). Methods for tracking support boundaries with corners. Journal of Multivariate Analysis. 97(8). 1870–1893.

13.

Cheng, Ming−Yen & Liang Peng. (2005). Simple and efficient improvements of multivariate local linear regression. Journal of Multivariate Analysis. 97(7). 1501–1524. 7 indexed citations

14.

Cheng, Ming−Yen, Jianqing Fan, & Vladimir Spokoiny. (2003). Dynamic nonparametric filtering with application to finance. Open MIND. 2 indexed citations

15.

Cheng, Ming−Yen & Peter Hall. (2002). ERROR-DEPENDENT SMOOTHING RULES IN LOCAL LINEAR REGRESSION. Statistica Sinica. 4 indexed citations

16.

Cheng, Ming−Yen, Théo Gasser, & Peter Hall. (1999). Nonparametric Density Estimation under Unimodality and Monotonicity Constraints. Journal of Computational and Graphical Statistics. 8(1). 1–21. 39 indexed citations

17.

Cheng, Ming−Yen & Peter Hall. (1998). Calibrating the Excess Mass and Dip Tests of Modality. Journal of the Royal Statistical Society Series B (Statistical Methodology). 60(3). 579–589. 68 indexed citations

18.

Cheng, Ming−Yen. (1997). Boundary Aware Estimators of Integrated Density Derivative Products. Journal of the Royal Statistical Society Series B (Statistical Methodology). 59(1). 191–203. 32 indexed citations

19.

Cheng, Ming−Yen. (1994). On boundary effects of smooth curve estimators. NCSU Libraries Repository (North Carolina State University Libraries). 5 indexed citations

20.

Huang, S.K., Ming−Yen Cheng, & S.W. Hui. (1990). Effect of lateral mobility of fluorescent probes in lipid mixing assays of cell fusion. Biophysical Journal. 58(5). 1119–1126. 8 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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