J. C. Wakefield

1.4k total citations
8 papers, 689 citations indexed

About

J. C. Wakefield is a scholar working on Statistics and Probability, Economics and Econometrics and Artificial Intelligence. According to data from OpenAlex, J. C. Wakefield has authored 8 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Statistics and Probability, 3 papers in Economics and Econometrics and 2 papers in Artificial Intelligence. Recurrent topics in J. C. Wakefield's work include Statistical Methods and Bayesian Inference (5 papers), Bayesian Methods and Mixture Models (2 papers) and Spatial and Panel Data Analysis (2 papers). J. C. Wakefield is often cited by papers focused on Statistical Methods and Bayesian Inference (5 papers), Bayesian Methods and Mixture Models (2 papers) and Spatial and Panel Data Analysis (2 papers). J. C. Wakefield collaborates with scholars based in United Kingdom, United States and Switzerland. J. C. Wakefield's co-authors include A. F. M. Smith, Alan E. Gelfand, Amy Racine-Poon, Margaret G. Ehm, Matthew R. Nelson, Charles Cox, Xiuwen Zheng, B. S. Weir, Judong Shen and James E. Bennett and has published in prestigious journals such as Journal of the Royal Statistical Society Series C (Applied Statistics), Statistical Methods in Medical Research and Biostatistics.

In The Last Decade

J. C. Wakefield

8 papers receiving 639 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. C. Wakefield United Kingdom 7 253 117 109 103 55 8 689
Gregg E. Dinse United States 21 547 2.2× 106 0.9× 67 0.6× 85 0.8× 61 1.1× 57 1.4k
Mark Sale United States 18 142 0.6× 35 0.3× 57 0.5× 33 0.3× 63 1.1× 42 1.1k
Lei Nie United States 22 659 2.6× 68 0.6× 304 2.8× 113 1.1× 44 0.8× 72 2.0k
Donald G. Thomas United States 13 256 1.0× 26 0.2× 37 0.3× 36 0.3× 68 1.2× 25 827
Mark Chang United States 12 441 1.7× 30 0.3× 68 0.6× 41 0.4× 24 0.4× 48 712
Mary Regina Boland United States 22 86 0.3× 346 3.0× 46 0.4× 85 0.8× 84 1.5× 72 1.3k
Seidu Inusah United States 11 117 0.5× 53 0.5× 43 0.4× 85 0.8× 61 1.1× 16 1.3k
James D. Cowan United States 9 49 0.2× 102 0.9× 32 0.3× 139 1.3× 41 0.7× 12 739
Telba Irony United States 15 222 0.9× 39 0.3× 14 0.1× 44 0.4× 32 0.6× 35 725
Sandro Gsteiger Switzerland 13 348 1.4× 15 0.1× 48 0.4× 31 0.3× 38 0.7× 26 654

Countries citing papers authored by J. C. Wakefield

Since Specialization
Citations

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

Fields of papers citing papers by J. C. Wakefield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. C. Wakefield. 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 J. C. Wakefield. The network helps show where J. C. Wakefield may publish in the future.

Co-authorship network of co-authors of J. C. Wakefield

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

All Works

8 of 8 papers shown
1.
Zheng, Xiuwen, Judong Shen, Charles Cox, et al.. (2013). HIBAG—HLA genotype imputation with attribute bagging. The Pharmacogenomics Journal. 14(2). 192–200. 245 indexed citations
2.
Dabney, Alan R. & J. C. Wakefield. (2005). Issues in the mapping of two diseases. Statistical Methods in Medical Research. 14(1). 83–112. 38 indexed citations
3.
Diggle, Peter J., Sara Morris, & J. C. Wakefield. (2000). Point-source modelling using matched case-control data. Biostatistics. 1(1). 89–105. 30 indexed citations
4.
Wakefield, J. C., et al.. (1999). The Bayesian analysis of a pivotal pharmacokinetic study. Statistical Methods in Medical Research. 8(3). 195–216. 6 indexed citations
5.
Bennett, James E., J. C. Wakefield, & L.F. Lacey. (1997). Modeling of Trough Plasma Bismuth Concentrations. Journal of Pharmacokinetics and Biopharmaceutics. 25(1). 79–106. 3 indexed citations
6.
Bennett, James E. & J. C. Wakefield. (1996). A comparison of a bayesian population method with two methods as implemented in commercially available software. Journal of Pharmacokinetics and Biopharmaceutics. 24(4). 403–432. 44 indexed citations
7.
Wakefield, J. C., A. F. M. Smith, Amy Racine-Poon, & Alan E. Gelfand. (1994). Bayesian Analysis of Linear and Non-Linear Population Models by Using the Gibbs Sampler. Journal of the Royal Statistical Society Series C (Applied Statistics). 43(1). 201–201. 227 indexed citations
8.
Wakefield, J. C., Alan E. Gelfand, & A. F. M. Smith. (1991). Efficient generation of random variates via the ratio-of-uniforms method. Statistics and Computing. 1(2). 129–133. 96 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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