Jin‐Guan Lin

973 total citations
125 papers, 678 citations indexed

About

Jin‐Guan Lin is a scholar working on Statistics and Probability, Management Science and Operations Research and Finance. According to data from OpenAlex, Jin‐Guan Lin has authored 125 papers receiving a total of 678 indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Statistics and Probability, 37 papers in Management Science and Operations Research and 27 papers in Finance. Recurrent topics in Jin‐Guan Lin's work include Statistical Methods and Inference (50 papers), Statistical Methods and Bayesian Inference (35 papers) and Advanced Statistical Methods and Models (33 papers). Jin‐Guan Lin is often cited by papers focused on Statistical Methods and Inference (50 papers), Statistical Methods and Bayesian Inference (35 papers) and Advanced Statistical Methods and Models (33 papers). Jin‐Guan Lin collaborates with scholars based in China, United Kingdom and United States. Jin‐Guan Lin's co-authors include Bo‐Cheng Wei, Feng‐Chang Xie, Chao Huang, Li Zhu, Yang Yang, Peirong Xu, Kaiyong Wang, Yong Li, Fangrong Yan and Ji Chen and has published in prestigious journals such as Journal of the American Statistical Association, PLoS ONE and Statistics in Medicine.

In The Last Decade

Jin‐Guan Lin

108 papers receiving 650 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jin‐Guan Lin China 15 455 199 158 105 71 125 678
Mogens Bladt Denmark 16 264 0.6× 205 1.0× 110 0.7× 184 1.8× 70 1.0× 42 686
Volker Krätschmer Germany 12 270 0.6× 403 2.0× 97 0.6× 144 1.4× 90 1.3× 32 512
Wai Kiu Chan United States 7 169 0.4× 109 0.5× 92 0.6× 49 0.5× 38 0.5× 21 451
J. H. Venter South Africa 12 262 0.6× 141 0.7× 119 0.8× 174 1.7× 54 0.8× 38 587
William P. McCormick United States 16 360 0.8× 188 0.9× 114 0.7× 435 4.1× 191 2.7× 70 833
Cheng–Der Fuh Taiwan 13 135 0.3× 99 0.5× 94 0.6× 145 1.4× 48 0.7× 57 426
Aldo Goia Italy 10 368 0.8× 132 0.7× 176 1.1× 64 0.6× 40 0.6× 22 619
Manuel Úbeda-Flores Spain 20 456 1.0× 374 1.9× 102 0.6× 747 7.1× 93 1.3× 77 1.1k
Graciela González–Farías Mexico 12 330 0.7× 112 0.6× 112 0.7× 201 1.9× 340 4.8× 44 878
Nicola Loperfido Italy 18 623 1.4× 166 0.8× 208 1.3× 254 2.4× 110 1.5× 46 915

Countries citing papers authored by Jin‐Guan Lin

Since Specialization
Citations

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

Fields of papers citing papers by Jin‐Guan Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin‐Guan Lin. 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 Jin‐Guan Lin. The network helps show where Jin‐Guan Lin may publish in the future.

Co-authorship network of co-authors of Jin‐Guan Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Jin‐Guan Lin. A scholar is included among the top collaborators of Jin‐Guan Lin 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 Jin‐Guan Lin. Jin‐Guan Lin 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.
Jiang, Xiaohu, et al.. (2023). Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data. Psychometrika. 88(3). 975–1001. 1 indexed citations
2.
Shi, Jianhong, et al.. (2023). A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors. Mathematics. 11(19). 4165–4165. 1 indexed citations
3.
4.
Kong, Xinbing, Jin‐Guan Lin, Cheng Liu, & Guangying Liu. (2021). Discrepancy Between Global and Local Principal Component Analysis on Large-Panel High-Frequency Data. Journal of the American Statistical Association. 118(542). 1333–1344. 12 indexed citations
5.
Liu, Guoxiang, et al.. (2018). Semiparametric jump-preserving estimation for single-index models. Journal of nonparametric statistics. 30(3). 556–580. 1 indexed citations
6.
Chen, Xueping, et al.. (2016). Designs containing partially clear main effects. Statistics & Probability Letters. 121. 12–17. 1 indexed citations
7.
Yan, Fangrong, et al.. (2014). Parameter Estimation of Population Pharmacokinetic Models with Stochastic Differential Equations: Implementation of an Estimation Algorithm. Journal of Probability and Statistics. 2014. 1–8. 3 indexed citations
8.
Lin, Jin‐Guan, et al.. (2013). Local Linear Estimation for Spatiotemporal Models Based on Least Absolute Deviation. Communication in Statistics- Theory and Methods. 44(7). 1508–1522. 1 indexed citations
9.
Liu, Junlin, et al.. (2013). Dose-Finding Based on Bivariate Efficacy-Toxicity Outcome Using Archimedean Copula. PLoS ONE. 8(11). e78805–e78805. 15 indexed citations
10.
Li, Yong, et al.. (2011). A Stochastic Simulation Approach to Model Selection for Stochastic Volatility Models. Communications in Statistics - Simulation and Computation. 40(7). 1043–1056. 4 indexed citations
11.
Lin, Jin‐Guan. (2009). Parameter Estimation and Equivalence between CDM and MSOM in Quantile Regression of Longitudinal Data Models with Fixed Effects. Mathematica Applicata. 1 indexed citations
12.
Lin, Jin‐Guan, Feng‐Chang Xie, & Bo‐Cheng Wei. (2009). Statistical Diagnostics for Skew-t-Normal Nonlinear Models. Communications in Statistics - Simulation and Computation. 38(10). 2096–2110. 18 indexed citations
13.
Xie, Feng‐Chang, Bo‐Cheng Wei, & Jin‐Guan Lin. (2009). Score tests for zero-inflated generalized Poisson mixed regression models. Computational Statistics & Data Analysis. 53(9). 3478–3489. 26 indexed citations
14.
Xie, Feng‐Chang, Bo‐Cheng Wei, & Jin‐Guan Lin. (2008). Assessing influence for pharmaceutical data in zero‐inflated generalized Poisson mixed models. Statistics in Medicine. 27(18). 3656–3673. 17 indexed citations
15.
Lin, Jin‐Guan & Bo‐Cheng Wei. (2007). Testing for Heteroscedasticity and/or Autocorrelation in Longitudinal Mixed Effect Nonlinear Models with AR(1) Errors. Communication in Statistics- Theory and Methods. 36(3). 567–586. 6 indexed citations
16.
Xie, Feng‐Chang, Bo‐Cheng Wei, & Jin‐Guan Lin. (2007). Case-deletion Influence Measures for the Data from Multivariate t Distributions. Journal of Applied Statistics. 34(8). 907–921. 6 indexed citations
17.
Lin, Jin‐Guan. (2006). Perturbation Diagnostics of Autocorrelation Coefficients in Nonlinear Models with Random Effects and AR(1) Errors. Mathematica Applicata. 1 indexed citations
18.
Lin, Jin‐Guan & Bo‐Cheng Wei. (2004). Testing for Heteroscedasticity and/or Correlation in Nonlinear Models with Correlated Errors. Communication in Statistics- Theory and Methods. 33(2). 251–275. 10 indexed citations
19.
Lin, Jin‐Guan. (2004). Testing for Autocorrelation and Presence of Random Effects in Nonlinear Models Based on Longitudinal Data. Mathematica Applicata. 1 indexed citations
20.
Lin, Jin‐Guan. (2002). Score Tests of Heteroscedasticity in Nonlinear Regression Models with Random Coefficients. Gongcheng shuxue xuebao.

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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