Chong Gu

3.5k total citations · 1 hit paper
48 papers, 2.1k citations indexed

About

Chong Gu is a scholar working on Statistics and Probability, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Chong Gu has authored 48 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Statistics and Probability, 10 papers in Artificial Intelligence and 8 papers in Control and Systems Engineering. Recurrent topics in Chong Gu's work include Statistical Methods and Inference (22 papers), Advanced Statistical Methods and Models (15 papers) and Statistical Methods and Bayesian Inference (8 papers). Chong Gu is often cited by papers focused on Statistical Methods and Inference (22 papers), Advanced Statistical Methods and Models (15 papers) and Statistical Methods and Bayesian Inference (8 papers). Chong Gu collaborates with scholars based in United States, Canada and Germany. Chong Gu's co-authors include Grace Wahba, Young-Ju Kim, Ronald Klein, Yuedong Wang, Ping Ma, Chunfu Qiu, Young‐Ju Kim, Zehua Chen, Jingyuan Wang and Dong Xiang and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and Statistics in Medicine.

In The Last Decade

Chong Gu

45 papers receiving 1.9k citations

Hit Papers

align trajectories

Peers

Countries citing papers authored by Chong Gu

Since Specialization

Citations

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

Fields of papers citing papers by Chong Gu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chong Gu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Discussion: Polynomial Splines and their Tensor Products in Extended Linear Modeling 2007 ·Chong Gu	0
2	Watermarking Algorithm on 2D Vector Digital Maps 2006 ·Wuhan Daxue xuebao. Xinxi kexue ban ·(unknown), Chong Gu, Liangpei Zhang	6
3	Penalized likelihood hazard estimation: Efficient approximation and Bayesian confidence intervals 2005 ·Statistics & Probability Letters ·Pang Du, Chong Gu	8
4	Smoothing spline regression: scalable computation and cross validation 2003 ·Purdue e-Pubs (Purdue University System) ·Young-Ju Kim, Chong Gu	2
5	PENALIZED LIKELIHOOD DENSITY ESTIMATION: DIRECT CROSS-VALIDATION AND SCALABLE APPROXIMATION 2003 ·Chong Gu, Jingyuan Wang	25
6	Cross-Validating Non-Gaussian Data: Generalized Approximate Cross-Validation Revisited 2001 ·Journal of Computational and Graphical Statistics ·Chong Gu, Dong Xiang	25
7	USING SMOOTHING SPLINE ANOVA TO EXAMINE THE RELATION OF RISK FACTORS TO THE INCIDENCE AND PROGRESSION OF DIABETIC RETINOPATHY 1997 ·Statistics in Medicine ·Yuedong Wang, Grace Wahba, Chong Gu, Ronald Klein	16
8	PENALIZED LIKELIHOOD HAZARD ESTIMATION: A GENERAL PROCEDURE 1996 ·Chong Gu	32
9	Simulation studies of smoothing parameter estimates and bayesian confidence intervals in bernoulli ss anova models 1995 ·Communications in Statistics - Simulation and Computation ·Yuedong Wang, Grace Wahba, Rick Chappell, Chong Gu	5
10	Structured Machine Learning for 'Soft' Classification with Smoothing Spline ANOVA and Stacked Tuning, Testing and Evaluation 1993 ·Neural Information Processing Systems ·Grace Wahba, Yuedong Wang, Chong Gu, Ronald Klein, Barbara E.K. Klein	15
11	Smoothing Spline Density Estimation: A Dimensionless Automatic Algorithm 1993 ·Journal of the American Statistical Association ·Chong Gu	42
12	Smoothing Spline ANOVA with Component-Wise Bayesian “Confidence Intervals” 1993 ·Journal of Computational and Graphical Statistics ·Chong Gu, Grace Wahba	68
13	Smoothing Spline Density Estimation: Theory 1993 ·The Annals of Statistics ·Chong Gu, Chunfu Qiu	65
14	Diagnostics for Nonparametric Regression Models with Additive Terms 1992 ·Journal of the American Statistical Association ·Chong Gu	4
15	Diagnostics for Nonparametric Regression Models with Additive Terms 1992 ·Journal of the American Statistical Association ·Chong Gu	30
16	Discussion: Multivariate Adaptive Regression Splines 1991 ·The Annals of Statistics ·Chong Gu, Grace Wahba	14
17	Adaptive Spline Smoothing in Non-Gaussion Regression Models 1990 ·Journal of the American Statistical Association ·Chong Gu	18
18	The Computation of Generalized Cross-Validation Functions Through Householder Tridiagonalization with Applications to the Fitting of Interaction Spline Models 1989 ·SIAM Journal on Matrix Analysis and Applications ·Chong Gu, Douglas M. Bates, Zehua Chen, Grace Wahba	49
19	Discussion: Linear Smoothers and Additive Models 1989 ·The Annals of Statistics ·Zehua Chen, Chong Gu, Grace Wahba	9
20	What happens when bootstrapping the smoothing spline 1987 ·Communication in Statistics- Theory and Methods ·Chong Gu	4

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.

Explore authors with similar magnitude of impact