Lu Lin

1.5k total citations · 1 hit paper
51 papers, 979 citations indexed

About

Lu Lin is a scholar working on Statistics and Probability, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Lu Lin has authored 51 papers receiving a total of 979 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Statistics and Probability, 9 papers in Artificial Intelligence and 8 papers in Control and Systems Engineering. Recurrent topics in Lu Lin's work include Statistical Methods and Inference (34 papers), Advanced Statistical Methods and Models (24 papers) and Statistical Methods and Bayesian Inference (8 papers). Lu Lin is often cited by papers focused on Statistical Methods and Inference (34 papers), Advanced Statistical Methods and Models (24 papers) and Statistical Methods and Bayesian Inference (8 papers). Lu Lin collaborates with scholars based in China, Hong Kong and United States. Lu Lin's co-authors include Paul Embrechts, Tiejun Tong, Jiandong Shi, Xian‐Tao Zeng, Dehui Luo, Hong Weng, Haitao Chu, Kangning Wang, Runchu Zhang and Minghua Chen and has published in prestigious journals such as Journal of the American Statistical Association, Energy Policy and PLoS Computational Biology.

In The Last Decade

Lu Lin

48 papers receiving 953 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optimally estimating the sample standard deviation from the five‐number summary

2020 400 citations Lu Lin et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lu Lin China	13	243	150	127	124	119	51	979
Yuedong Wang United States	30	459 1.9×	51 0.3×	132 1.0×	26 0.2×	378 3.2×	112	2.6k
Sara López‐Pintado United States	15	388 1.6×	55 0.4×	191 1.5×	17 0.1×	42 0.4×	33	1.2k
Tetsuro Hida Japan	24	79 0.3×	132 0.9×	68 0.5×	187 1.5×	1.1k 8.8×	85	2.2k
Weimin Huang China	14	282 1.2×	17 0.1×	89 0.7×	45 0.4×	189 1.6×	56	1.2k
Yuedong Wang United States	16	434 1.8×	51 0.3×	37 0.3×	48 0.4×	28 0.2×	48	962
Douglas G. Simpson United States	24	1.1k 4.7×	57 0.4×	55 0.4×	52 0.4×	43 0.4×	75	1.9k
Alex Karagrigoriou Greece	14	289 1.2×	19 0.1×	115 0.9×	59 0.5×	31 0.3×	78	795
Quefeng Li United States	19	245 1.0×	11 0.1×	109 0.9×	23 0.2×	96 0.8×	79	1.1k
Gilles R. Ducharme Canada	15	226 0.9×	16 0.1×	41 0.3×	43 0.3×	80 0.7×	74	865
Lothar Heinrich Germany	20	228 0.9×	433 2.9×	50 0.4×	112 0.9×	15 0.1×	91	1.2k

Countries citing papers authored by Lu Lin

Since Specialization

Citations

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

Fields of papers citing papers by Lu Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lu Lin

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

1.

Ye, Jiayu, et al.. (2025). MFE-Former: Disentangling Emotion-Identity Dynamics via Self-Supervised Learning for Enhancing Speech-Driven Depression Detection. IEEE Journal of Biomedical and Health Informatics. 30(3). 2049–2060.

2.

Lin, Lu, et al.. (2024). Weighted likelihood transfer learning for high-dimensional generalized linear models. Statistics. 58(3). 690–722.

3.

Lin, Lu, Kangning Wang, & Shaomin Li. (2020). Copula based composite quantile regression for longitudinal data and variable selection. Scientia Sinica Mathematica. 50(8). 1097–1097. 1 indexed citations

4.

Wang, Kangning & Lu Lin. (2020). Variable Selection for Varying Coefficient Models Via Kernel Based Regularized Rank Regression. Acta Mathematicae Applicatae Sinica English Series. 36(2). 458–470. 1 indexed citations

5.

Jiang, Hongmei, et al.. (2018). A marginalized two-part Beta regression model for microbiome compositional data. PLoS Computational Biology. 14(7). e1006329–e1006329. 23 indexed citations

6.

Zhu, Lixing, et al.. (2017). Upper expectation parametric regression. Statistica Sinica. 3 indexed citations

7.

Wang, Kangning & Lu Lin. (2016). Robust structure identification and variable selection in partial linear varying coefficient models. Journal of Statistical Planning and Inference. 174. 153–168. 19 indexed citations

8.

You, Wenjie, et al.. (2015). Covariance matrix and transfer function of dynamic generalized linear models. Journal of Computational and Applied Mathematics. 296. 613–624. 2 indexed citations

9.

Guo, Xu, et al.. (2014). Testing for positive expectation dependence. Annals of the Institute of Statistical Mathematics. 68(1). 135–153. 6 indexed citations

10.

Zhang, Qi & Lu Lin. (2013). Terminal-Dependent Statistical Inferences for FBSDE. Stochastic Analysis and Applications. 32(1). 128–151. 1 indexed citations

11.

Jian, Ling, et al.. (2013). Block Empirical Likelihood for Longitudinal Single-Index Varying-Coefficient Model. Journal of Applied Mathematics. 2013. 1–10. 1 indexed citations

12.

Lin, Lu, et al.. (2013). Local linear–additive estimation for multiple nonparametric regressions. Journal of Multivariate Analysis. 123. 252–269. 1 indexed citations

13.

Embrechts, Paul, et al.. (2011). Multivariate Hawkes processes: an application to financial data. Journal of Applied Probability. 48(A). 367–378. 87 indexed citations

14.

Embrechts, Paul, et al.. (2011). Multivariate Hawkes processes: an application to financial data. Journal of Applied Probability. 48(A). 367–378. 167 indexed citations

15.

Chen, Xi & Lu Lin. (2010). Nonparametric Estimation for FBSDEs Models with Applications in Finance. Communication in Statistics- Theory and Methods. 39(14). 2492–2514. 3 indexed citations

16.

Lin, Lu, et al.. (2009). Semi-Parametric Estimation for Forward–Backward Stochastic Differential Equations. Communication in Statistics- Theory and Methods. 38(11). 1759–1775. 4 indexed citations

17.

Cui, Xia, et al.. (2008). An Extended Projection Data Depth and Its Applications to Discrimination. Communication in Statistics- Theory and Methods. 37(14). 2276–2290. 12 indexed citations

18.

Lin, Lu & Xia Cui. (2006). Stahel-Donoho kernel estimation for fixed design nonparametric regression models. Science in China Series A Mathematics. 49(12). 1879–1896. 2 indexed citations

19.

Lin, Lu, et al.. (2006). Blockwise bootstrap wavelet in nonparametric regression model with weakly dependent processes. Metrika. 67(1). 31–48. 2 indexed citations

20.

Lin, Lu & Runchu Zhang. (2001). Blockwise empirical Euclidean likelihood for weakly dependent processes. Statistics & Probability Letters. 53(2). 143–152. 19 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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