Jane‐Ling Wang

1.3k total citations
18 papers, 867 citations indexed

About

Jane‐Ling Wang is a scholar working on Insect Science, Statistics and Probability and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Jane‐Ling Wang has authored 18 papers receiving a total of 867 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Insect Science, 6 papers in Statistics and Probability and 3 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Jane‐Ling Wang's work include Insect behavior and control techniques (6 papers), Statistical Methods and Inference (5 papers) and Insect Utilization and Effects (4 papers). Jane‐Ling Wang is often cited by papers focused on Insect behavior and control techniques (6 papers), Statistical Methods and Inference (5 papers) and Insect Utilization and Effects (4 papers). Jane‐Ling Wang collaborates with scholars based in United States, Mexico and Greece. Jane‐Ling Wang's co-authors include Hans‐Georg Müller, James R. Carey, Fushing Hsieh, Yi‐Kuan Tseng, Pablo Liedo, Lawrence G. Harshman, Zhen Zhang, Ying Zhang, Nikos T. Papadopoulos and Linda Partridge and has published in prestigious journals such as Biometrics, Statistics in Medicine and Human Brain Mapping.

In The Last Decade

Jane‐Ling Wang

18 papers receiving 837 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jane‐Ling Wang United States	15	284	205	116	106	91	18	867
Richard J. Boys United Kingdom	18	90 0.3×	7 0.0×	86 0.7×	34 0.3×	82 0.9×	47	1.1k
Sihai Dave Zhao United States	19	242 0.9×	59 0.3×	4 0.0×	115 1.1×	163 1.8×	43	906
Jane-Ling Wang United States	7	133 0.5×	30 0.1×	17 0.1×	12 0.1×	11 0.1×	7	499
Kevin Flores United States	13	18 0.1×	57 0.3×	15 0.1×	40 0.4×	113 1.2×	43	687
Kamil Erguler Cyprus	14	34 0.1×	26 0.1×	4 0.0×	18 0.2×	68 0.7×	22	700
Ioannis Kosmidis United Kingdom	14	203 0.7×	15 0.1×	2 0.0×	39 0.4×	32 0.4×	40	671
Jordi Ocaña Spain	11	73 0.3×	137 0.7×	4 0.0×	86 0.8×	156 1.7×	43	497
Anne Gégout‐Petit France	12	140 0.5×	17 0.1×	11 0.1×	6 0.1×	20 0.2×	28	422
Leonardo Bottolo United Kingdom	18	48 0.2×	17 0.1×	4 0.0×	27 0.3×	795 8.7×	40	1.7k
Matthew P. Conomos United States	16	27 0.1×	15 0.1×	9 0.1×	24 0.2×	861 9.5×	26	1.5k

Countries citing papers authored by Jane‐Ling Wang

Since Specialization

Citations

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

Fields of papers citing papers by Jane‐Ling Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jane‐Ling Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Jane‐Ling Wang. A scholar is included among the top collaborators of Jane‐Ling 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 Jane‐Ling Wang. Jane‐Ling 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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18 of 18 papers shown

Zhu, Changbo, Hans‐Georg Müller, Jane‐Ling Wang, et al.. (2023). Trajectories of brain volumes in young children are associated with maternal education. Human Brain Mapping. 44(8). 3168–3179. 3 indexed citations

Dai, Xiongtao, et al.. (2019). Longitudinal associations between white matter maturation and cognitive development across early childhood. Human Brain Mapping. 40(14). 4130–4145. 29 indexed citations

Chen, Kehui, et al.. (2015). Female access and diet affect insemination success, senescence and the cost of reproduction in the male M exican fruit fly A nastrepha ludens . Physiological Entomology. 40(1). 65–71. 14 indexed citations

Wu, Jia‐Feng, Yu‐Ru Su, Chen‐Hsin Chen, et al.. (2011). Predictive Effect of Serial Serum Alanine Aminotransferase Levels on Spontaneous HBeAg Seroconversion in Chronic Genotype B and C HBV–Infected Children. Journal of Pediatric Gastroenterology and Nutrition. 54(1). 97–100. 29 indexed citations

Kouloussis, Nikos A., Nikos T. Papadopoulos, Hans‐Georg Müller, et al.. (2009). Life table assay of field‐caught Mediterranean fruit flies, Ceratitis capitata, reveals age bias. Entomologia Experimentalis et Applicata. 132(2). 172–181. 14 indexed citations

Molleman, Freerk, et al.. (2008). Adult diet affects lifespan and reproduction of the fruit‐feeding butterflyCharaxes fulvescens. Entomologia Experimentalis et Applicata. 129(1). 54–65. 22 indexed citations

Carey, James R., Lawrence G. Harshman, Pablo Liedo, et al.. (2008). Longevity–fertility trade‐offs in the tephritid fruit fly,Anastrepha ludens, across dietary‐restriction gradients. Aging Cell. 7(4). 470–477. 104 indexed citations

Molleman, Freerk, et al.. (2008). Amino acid sources in the adult diet do not affect life span and fecundity in the fruit‐feeding butterfly Bicyclus anynana. Ecological Entomology. 33(4). 429–438. 24 indexed citations

Carey, James R., Nikos T. Papadopoulos, Hans‐Georg Müller, et al.. (2008). Age structure changes and extraordinary lifespan in wild medfly populations. Aging Cell. 7(3). 426–437. 39 indexed citations

10.

Wang, Jane‐Ling, et al.. (2007). Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data. Biometrics. 64(2). 546–556. 58 indexed citations

11.

Hsieh, Fushing, Yi‐Kuan Tseng, & Jane‐Ling Wang. (2006). Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited. Biometrics. 62(4). 1037–1043. 133 indexed citations

12.

Müller, Hans‐Georg, Jane‐Ling Wang, James R. Carey, et al.. (2004). Demographic window to aging in the wild: constructing life tables and estimating survival functions from marked individuals of unknown age. Aging Cell. 3(3). 125–131. 49 indexed citations

13.

Carey, James R., Pablo Liedo, Hans‐Georg Müller, et al.. (2004). Stochastic dietary restriction using a Markov‐chain feeding protocol elicits complex, life history response in medflies. Aging Cell. 4(1). 31–39. 23 indexed citations

14.

Carey, James R., Pablo Liedo, Lawrence G. Harshman, et al.. (2002). Life history response of Mediterranean fruit flies to dietary restriction. Aging Cell. 1(2). 140–148. 85 indexed citations

15.

Dubin, Joel A., Hans‐Georg Müller, & Jane‐Ling Wang. (2001). Event history graphs for censored survival data. Statistics in Medicine. 20(19). 2951–2964. 10 indexed citations

16.

Wang, Jane‐Ling. (1999). Asymptotic Properties of M‐estimators Based on Estimating Equations and Censored Data. Scandinavian Journal of Statistics. 26(2). 297–318. 15 indexed citations

17.

Müller, Hans‐Georg & Jane‐Ling Wang. (1994). Hazard Rate Estimation under Random Censoring with Varying Kernels and Bandwidths. Biometrics. 50(1). 61–61. 214 indexed citations

18.

Müller, Hans‐Georg & Jane‐Ling Wang. (1990). Bootstrap Confidence Intervals for Effective Doses in the Probit Model for Dose‐Response Data. Biometrical Journal. 32(5). 529–544. 2 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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