Lianming Wang

944 total citations
32 papers, 637 citations indexed

About

Lianming Wang is a scholar working on Statistics and Probability, Artificial Intelligence and Economics and Econometrics. According to data from OpenAlex, Lianming Wang has authored 32 papers receiving a total of 637 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistics and Probability, 10 papers in Artificial Intelligence and 3 papers in Economics and Econometrics. Recurrent topics in Lianming Wang's work include Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (24 papers) and Bayesian Methods and Mixture Models (10 papers). Lianming Wang is often cited by papers focused on Statistical Methods and Inference (26 papers), Statistical Methods and Bayesian Inference (24 papers) and Bayesian Methods and Mixture Models (10 papers). Lianming Wang collaborates with scholars based in United States, China and Hong Kong. Lianming Wang's co-authors include Xiaoyan Lin, Christopher S. McMahan, David B. Dunson, Jianguo Sun, Bo Cai, Zaina P. Qureshi, Michael G. Hudgens, Xingwei Tong, Liuquan Sun and Joshua M. Tebbs and has published in prestigious journals such as Biometrics, Biometrika and Statistics in Medicine.

In The Last Decade

Lianming Wang

31 papers receiving 630 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lianming Wang United States	15	473	163	66	60	45	32	637
Egil Ferkingstad Norway	10	183 0.4×	52 0.3×	67 1.0×	150 2.5×	8 0.2×	16	442
Naitee Ting United States	11	260 0.5×	11 0.1×	66 1.0×	46 0.8×	23 0.5×	56	553
Wenge Guo United States	14	134 0.3×	18 0.1×	14 0.2×	160 2.7×	28 0.6×	32	446
Amparo Baı́llo Spain	9	183 0.4×	89 0.5×	22 0.3×	24 0.4×	20 0.4×	21	283
Qi Jiang United States	12	195 0.4×	44 0.3×	130 2.0×	21 0.3×	8 0.2×	37	382
Xingwei Tong China	17	604 1.3×	129 0.8×	141 2.1×	51 0.8×	5 0.1×	68	760
Hilario Navarro Spain	11	64 0.1×	37 0.2×	5 0.1×	129 2.1×	54 1.2×	28	340
Stephanie J. Reisinger United States	9	51 0.1×	40 0.2×	42 0.6×	80 1.3×	9 0.2×	10	322
Zhaoling Meng United States	9	85 0.2×	16 0.1×	39 0.6×	131 2.2×	6 0.1×	21	356

Countries citing papers authored by Lianming Wang

Since Specialization

Citations

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

Fields of papers citing papers by Lianming Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lianming Wang

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

Hu, Tao, et al.. (2024). Regression analysis of group-tested current status data. Biometrika. 111(3). 1047–1061. 3 indexed citations

Chakrabarti, Mrinmay, Kai Jiao, Jay D. Potts, et al.. (2023). Hippo Signaling Mediates TGFβ-Dependent Transcriptional Inputs in Cardiac Cushion Mesenchymal Cells to Regulate Extracellular Matrix Remodeling. Journal of Cardiovascular Development and Disease. 10(12). 483–483. 3 indexed citations

McMahan, Christopher S., et al.. (2022). A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model. Lifetime Data Analysis. 29(1). 188–212. 4 indexed citations

Sun, Liuquan, et al.. (2021). A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup. Statistical Methods in Medical Research. 30(8). 1890–1903. 4 indexed citations

Sun, Liuquan, et al.. (2021). Simultaneous variable selection in regression analysis of multivariate interval‐censored data. Biometrics. 78(4). 1402–1413. 10 indexed citations

Wang, Lianming, et al.. (2021). Regression analysis of arbitrarily censored survival data under the proportional odds model. Statistics in Medicine. 40(16). 3724–3739. 5 indexed citations

Wang, Lu, et al.. (2020). An EM algorithm for analyzing right-censored survival data under the semiparametric proportional odds model. Communication in Statistics- Theory and Methods. 51(15). 5284–5297. 2 indexed citations

Liu, Qing, Johnie Hodge, Junfeng Wang, et al.. (2020). Emodin reduces Breast Cancer Lung Metastasis by suppressing Macrophage-induced Breast Cancer Cell Epithelial-mesenchymal transition and Cancer Stem Cell formation. Theranostics. 10(18). 8365–8381. 102 indexed citations

Cai, Bo, et al.. (2020). A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model. Statistical Methods in Medical Research. 29(11). 3192–3204. 18 indexed citations

10.

Zhang, Jiajia, et al.. (2020). Semiparametric estimation of the cure fraction in population‐based cancer survival analysis. Statistics in Medicine. 39(26). 3787–3805.

11.

Sun, Liuquan, et al.. (2019). Variable selection in semiparametric nonmixture cure model with interval‐censored failure time data: An application to the prostate cancer screening study. Statistics in Medicine. 38(16). 3026–3039. 14 indexed citations

12.

McMahan, Christopher S., et al.. (2018). A Gamma-frailty proportional hazards model for bivariate interval-censored data. Computational Statistics & Data Analysis. 128. 354–366. 6 indexed citations

13.

Cui, Weigang, et al.. (2018). [Impact of PRDM1 gene inactivation on C-MYC regulation in diffuse large B-cell lymphoma].. PubMed. 47(1). 25–31. 5 indexed citations

14.

Lin, Xiaoyan, Bo Cai, Lianming Wang, & Zhigang Zhang. (2014). A Bayesian proportional hazards model for general interval-censored data. Lifetime Data Analysis. 21(3). 470–490. 20 indexed citations

15.

Cai, Bo, Xiaoyan Lin, & Lianming Wang. (2011). Bayesian proportional hazards model for current status data with monotone splines. Computational Statistics & Data Analysis. 55(9). 2644–2651. 36 indexed citations

16.

Wang, Lianming & David B. Dunson. (2010). Fast Bayesian Inference in Dirichlet Process Mixture Models. Journal of Computational and Graphical Statistics. 20(1). 196–216. 48 indexed citations

17.

Wang, Lianming & David B. Dunson. (2010). Semiparametric Bayes' Proportional Odds Models for Current Status Data with Underreporting. Biometrics. 67(3). 1111–1118. 23 indexed citations

18.

Wang, Lianming & David B. Dunson. (2009). Semiparametric Bayes Multiple Testing: Applications to Tumor Data. Biometrics. 66(2). 493–501. 6 indexed citations

19.

Wang, Lianming, Jianguo Sun, & Xingwei Tong. (2007). Efficient estimation for the proportional hazards model with bivariate current status data. Lifetime Data Analysis. 14(2). 134–153. 28 indexed citations

20.

Wang, Lianming, Liuquan Sun, & Jianguo Sun. (2006). A Goodness‐of‐fit Test for the Marginal Cox Model for Correlated Interval‐censored Failure Time Data. Biometrical Journal. 48(6). 1020–1028. 6 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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