José E. Chacón

933 total citations
22 papers, 502 citations indexed

About

José E. Chacón is a scholar working on Statistics and Probability, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, José E. Chacón has authored 22 papers receiving a total of 502 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Statistics and Probability, 12 papers in Artificial Intelligence and 8 papers in Control and Systems Engineering. Recurrent topics in José E. Chacón's work include Statistical Methods and Inference (11 papers), Control Systems and Identification (8 papers) and Bayesian Methods and Mixture Models (5 papers). José E. Chacón is often cited by papers focused on Statistical Methods and Inference (11 papers), Control Systems and Identification (8 papers) and Bayesian Methods and Mixture Models (5 papers). José E. Chacón collaborates with scholars based in Spain, France and Portugal. José E. Chacón's co-authors include Tarn Duong, M. P. Wand, Alberto Rodríguez‐Casal, Agustín García Nogales, Jesús Montanero‐Fernández, Carlos Tenreiro, Glòria Mateu‐Figueras, Josep Antoni Martín Fernández and Amparo Baı́llo and has published in prestigious journals such as Computers & Geosciences, Computational Statistics & Data Analysis and Statistics and Computing.

In The Last Decade

José E. Chacón

22 papers receiving 487 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
José E. Chacón Spain 12 204 203 59 45 37 22 502
Bernard D. Flury United States 13 218 1.1× 240 1.2× 30 0.5× 87 1.9× 36 1.0× 29 663
R. Frühwirth Austria 18 257 1.3× 185 0.9× 47 0.8× 64 1.4× 10 0.3× 119 1.1k
Debdeep Pati United States 13 319 1.6× 350 1.7× 32 0.5× 29 0.6× 18 0.5× 46 714
Nicholas A. James United States 4 88 0.4× 131 0.6× 36 0.6× 23 0.5× 45 1.2× 7 477
Michiel Debruyne Belgium 11 163 0.8× 212 1.0× 93 1.6× 70 1.6× 14 0.4× 16 634
Pushpa N. Rathie Brazil 14 138 0.7× 172 0.8× 31 0.5× 19 0.4× 49 1.3× 91 735
Hajo Holzmann Germany 13 207 1.0× 262 1.3× 17 0.3× 37 0.8× 19 0.5× 58 583
Matthew A. Nunes United Kingdom 9 181 0.9× 160 0.8× 23 0.4× 44 1.0× 11 0.3× 27 450
Inge Koch Australia 12 111 0.5× 111 0.5× 47 0.8× 70 1.6× 11 0.3× 55 479
Hee‐Seok Oh South Korea 16 142 0.7× 269 1.3× 140 2.4× 96 2.1× 13 0.4× 91 989

Countries citing papers authored by José E. Chacón

Since Specialization
Citations

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

Fields of papers citing papers by José E. Chacón

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José E. Chacón. 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 José E. Chacón. The network helps show where José E. Chacón may publish in the future.

Co-authorship network of co-authors of José E. Chacón

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

All Works

20 of 20 papers shown
1.
Chacón, José E., et al.. (2024). Bayesian taut splines for estimating the number of modes. Computational Statistics & Data Analysis. 196. 107961–107961. 1 indexed citations
2.
Chacón, José E., et al.. (2023). Bump hunting through density curvature features. Test. 32(4). 1251–1275. 1 indexed citations
3.
Chacón, José E., et al.. (2022). Minimum adjusted Rand index for two clusterings of a given size. Advances in Data Analysis and Classification. 17(1). 125–133. 28 indexed citations
4.
Chacón, José E.. (2020). Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals. Journal of Classification. 38(2). 257–263. 1 indexed citations
5.
Baı́llo, Amparo & José E. Chacón. (2020). A new selection criterion for statistical home range estimation. Journal of Applied Statistics. 49(3). 722–737. 1 indexed citations
6.
Chacón, José E.. (2018). Mixture model modal clustering. Advances in Data Analysis and Classification. 13(2). 379–404. 16 indexed citations
7.
Chacón, José E. & Tarn Duong. (2014). Efficient recursive algorithms for functionals based on higher order derivatives of the multivariate Gaussian density. Statistics and Computing. 25(5). 959–974. 3 indexed citations
8.
Chacón, José E., et al.. (2013). Fourier methods for smooth distribution function estimation. Statistics & Probability Letters. 84. 223–230. 2 indexed citations
9.
Chacón, José E. & Carlos Tenreiro. (2013). Data-Based Choice of the Number of Pilot Stages for Plug-in Bandwidth Selection. Communication in Statistics- Theory and Methods. 42(12). 2200–2214. 3 indexed citations
10.
Chacón, José E. & Tarn Duong. (2012). Data-driven density derivative estimation, with applications to nonparametric clustering and bump hunting. arXiv (Cornell University). 52 indexed citations
11.
Chacón, José E., Tarn Duong, & M. P. Wand. (2011). Asymptotics for general multivariate kernel density derivative estimators. Statistica Sinica. 21(2). 807–807. 74 indexed citations
12.
Chacón, José E. & Tarn Duong. (2011). UNCONSTRAINED PILOT SELECTORS FOR SMOOTHED CROSS-VALIDATION. Australian & New Zealand Journal of Statistics. 53(3). 331–351. 19 indexed citations
13.
Chacón, José E. & Carlos Tenreiro. (2011). Exact and Asymptotically Optimal Bandwidths for Kernel Estimation of Density Functionals. Methodology And Computing In Applied Probability. 14(3). 523–548. 6 indexed citations
14.
Chacón, José E. & Alberto Rodríguez‐Casal. (2010). A note on the universal consistency of the kernel distribution function estimator. Statistics & Probability Letters. 80(17-18). 1414–1419. 15 indexed citations
15.
Chacón, José E., Glòria Mateu‐Figueras, & Josep Antoni Martín Fernández. (2010). Gaussian kernels for density estimation with compositional data. Computers & Geosciences. 37(5). 702–711. 11 indexed citations
16.
Chacón, José E. & Tarn Duong. (2009). Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices. Test. 19(2). 375–398. 85 indexed citations
17.
Chacón, José E., et al.. (2007). On the Use of Bayes Factor in Frequentist Testing of a Precise Hypothesis. Communication in Statistics- Theory and Methods. 36(12). 2251–2261. 1 indexed citations
18.
Chacón, José E., Jesús Montanero‐Fernández, & Agustín García Nogales. (2007). A note on kernel density estimation at a parametric rate†. Journal of nonparametric statistics. 19(1). 13–21. 18 indexed citations
19.
Chacón, José E., Jesús Montanero‐Fernández, & Agustín García Nogales. (2007). Bootstrap Bandwidth Selection Using anh‐Dependent Pilot Bandwidth. Scandinavian Journal of Statistics. 35(1). 139–157. 11 indexed citations
20.
Chacón, José E. & Alberto Rodríguez‐Casal. (2005). On theL1-consistency of wavelet density estimates. Canadian Journal of Statistics. 33(4). 489–496. 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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