Nema Dean

1.4k total citations
28 papers, 860 citations indexed

About

Nema Dean is a scholar working on Artificial Intelligence, Economics and Econometrics and Information Systems. According to data from OpenAlex, Nema Dean has authored 28 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Economics and Econometrics and 5 papers in Information Systems. Recurrent topics in Nema Dean's work include Bayesian Methods and Mixture Models (6 papers), Advanced Clustering Algorithms Research (6 papers) and Bayesian Modeling and Causal Inference (5 papers). Nema Dean is often cited by papers focused on Bayesian Methods and Mixture Models (6 papers), Advanced Clustering Algorithms Research (6 papers) and Bayesian Modeling and Causal Inference (5 papers). Nema Dean collaborates with scholars based in United Kingdom, United States and Australia. Nema Dean's co-authors include Adrian E. Raftery, Rebecca Nugent, Duncan Lee, Gérard Downey, Thomas Brendan Murphy, Erica E. M. Moodie, Yue Sun, Gwilym Pryce, Aneta Piekut and Guanpeng Dong and has published in prestigious journals such as Journal of the American Statistical Association, Analytica Chimica Acta and BMC Bioinformatics.

In The Last Decade

Nema Dean

26 papers receiving 822 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nema Dean United Kingdom	13	376	172	137	76	72	28	860
Ram Shanmugam United States	8	289 0.8×	120 0.7×	100 0.7×	95 1.3×	21 0.3×	16	1.1k
Madelyn Glymour United States	4	480 1.3×	183 1.1×	51 0.4×	78 1.0×	19 0.3×	5	1.1k
Beatriz de la Iglesia United Kingdom	16	353 0.9×	18 0.1×	135 1.0×	42 0.6×	71 1.0×	47	1.0k
Isobel Claire Gormley Ireland	17	222 0.6×	116 0.7×	153 1.1×	38 0.5×	21 0.3×	44	744
Kevin Small United States	17	526 1.4×	37 0.2×	128 0.9×	37 0.5×	57 0.8×	34	1.1k
Sharon Lee Australia	16	451 1.2×	372 2.2×	69 0.5×	46 0.6×	21 0.3×	59	1.1k
G. Govaert France	9	698 1.9×	266 1.5×	132 1.0×	57 0.8×	15 0.2×	14	1.1k
Dost Muhammad Khan Pakistan	18	151 0.4×	172 1.0×	127 0.9×	54 0.7×	7 0.1×	76	902
Yanqing Sun United States	16	163 0.4×	483 2.8×	21 0.2×	97 1.3×	80 1.1×	95	1.1k
C.M. Triggs New Zealand	16	110 0.3×	62 0.4×	151 1.1×	53 0.7×	22 0.3×	38	824

Countries citing papers authored by Nema Dean

Since Specialization

Citations

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

Fields of papers citing papers by Nema Dean

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nema Dean

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

Moodie, Erica E. M., et al.. (2024). Revisiting the effects of maternal education on adolescents’ academic performance: Doubly robust estimation in a network-based observational study. Journal of the Royal Statistical Society Series C (Applied Statistics). 73(3). 715–734.

Dean, Nema, et al.. (2024). COVID-19 vaccine fatigue in Scotland: how do the trends in attrition rates for the second and third doses differ by age, sex, and council area?. Journal of the Royal Statistical Society Series A (Statistics in Society). 188(1). 271–286.

Mechelen, Iven Van, Anne‐Laure Boulesteix, Nema Dean, et al.. (2023). A white paper on good research practices in benchmarking: The case of cluster analysis. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 13(6). 10 indexed citations

Dean, Nema, et al.. (2022). National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?. Spatial and Spatio-temporal Epidemiology. 44. 100559–100559. 6 indexed citations

Lee, Duncan, et al.. (2019). Estimating the Changing Nature of Scotland's Health Inequalities by using a Multivariate Spatiotemporal Model. Journal of the Royal Statistical Society Series A (Statistics in Society). 182(3). 1061–1080. 5 indexed citations

Dean, Nema, Adrian E. Raftery, & Luca Scrucca. (2018). Variable Selection for Gaussian Model-Based Clustering [R package clustvarsel version 2.3.3]. 1 indexed citations

Martyna, Agnieszka, et al.. (2016). Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta. 931. 34–46. 15 indexed citations

Dean, Nema, et al.. (2016). A Survey of Popular R Packages for Cluster Analysis. Journal of Educational and Behavioral Statistics. 41(2). 205–225. 36 indexed citations

Lee, Duncan, et al.. (2015). Bayesian cluster detection via adjacency modelling. Spatial and Spatio-temporal Epidemiology. 16. 11–20. 11 indexed citations

10.

Lee, Duncan, et al.. (2014). Identifying clusters in Bayesian disease mapping. Biostatistics. 15(3). 457–469. 46 indexed citations

11.

Dean, Nema & Rebecca Nugent. (2013). Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas. Advances in Data Analysis and Classification. 7(3). 339–357. 5 indexed citations

12.

Moodie, Erica E. M., Nema Dean, & Yue Sun. (2013). Q-Learning: Flexible Learning About Useful Utilities. Statistics in Biosciences. 6(2). 223–243. 55 indexed citations

13.

Dean, Nema & Rebecca Nugent. (2011). Comparing different clustering models on the unit hypercube. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1 indexed citations

14.

Nugent, Rebecca, et al.. (2010). Skill set profile clustering: the empty K-means algorithm with automatic specification of starting cluster centers. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 151–160. 18 indexed citations

15.

Nugent, Rebecca, et al.. (2009). Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students.. Educational Data Mining. 101–110. 3 indexed citations

16.

Nugent, Rebecca, et al.. (2009). A Comparison of Student Skill Knowledge Estimates. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 1–10. 31 indexed citations

17.

Nugent, Rebecca, et al.. (2009). Conditional subspace clustering of skill mastery: identifying skills that separate students. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 13 indexed citations

18.

Nugent, Rebecca, et al.. (2008). Skill Set Profile Clustering Based on Weighted Student Responses.. Educational Data Mining. 210–217. 4 indexed citations

19.

Raftery, Adrian E. & Nema Dean. (2006). Variable Selection for Model-Based Clustering. Journal of the American Statistical Association. 101(473). 168–178. 315 indexed citations

20.

Dean, Nema & Adrian E. Raftery. (2005). Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics. 6(1). 173–173. 46 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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