Daniel A. Henderson

947 total citations
21 papers, 433 citations indexed

About

Daniel A. Henderson is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Daniel A. Henderson has authored 21 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Statistics and Probability and 6 papers in Molecular Biology. Recurrent topics in Daniel A. Henderson's work include Bayesian Methods and Mixture Models (9 papers), Statistical Methods and Bayesian Inference (4 papers) and Gene Regulatory Network Analysis (4 papers). Daniel A. Henderson is often cited by papers focused on Bayesian Methods and Mixture Models (9 papers), Statistical Methods and Bayesian Inference (4 papers) and Gene Regulatory Network Analysis (4 papers). Daniel A. Henderson collaborates with scholars based in United Kingdom and United States. Daniel A. Henderson's co-authors include Richard J. Boys, Darren J. Wilkinson, M. C. Jones, Peter Avery, Michael S. Floyd, R. J. Boys, P. N. Sanda, Kim J. Krishnan, Andrew Golightly and Conor Lawless and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and Biometrics.

In The Last Decade

Daniel A. Henderson

20 papers receiving 418 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel A. Henderson United Kingdom	13	153	101	90	88	51	21	433
Jan Lemeire Belgium	9	172 1.1×	36 0.4×	31 0.3×	30 0.3×	37 0.7×	43	387
Nathanael A. Heckert Egypt	3	162 1.1×	37 0.4×	7 0.1×	50 0.6×	41 0.8×	4	450
仁大西	2	246 1.6×	18 0.2×	6 0.1×	39 0.4×	15 0.3×	3	399
Joe Suzuki Japan	8	349 2.3×	32 0.3×	35 0.4×	18 0.2×	3 0.1×	52	449
Chris S. Wallace Australia	7	208 1.4×	25 0.2×	32 0.4×	19 0.2×	4 0.1×	14	327
Wei Zhong United States	9	170 1.1×	94 0.9×	15 0.2×	19 0.2×	5 0.1×	34	414
Xiaolin Chen China	11	160 1.0×	13 0.1×	32 0.4×	40 0.5×	48 0.9×	61	408
T. Krishnan India	8	109 0.7×	26 0.3×	64 0.7×	32 0.4×	1 0.0×	28	301
Anastasios N. Angelopoulos United States	7	97 0.6×	27 0.3×	25 0.3×	19 0.2×	2 0.0×	20	275

Countries citing papers authored by Daniel A. Henderson

Since Specialization

Citations

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

Fields of papers citing papers by Daniel A. Henderson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel A. Henderson. 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 Daniel A. Henderson. The network helps show where Daniel A. Henderson may publish in the future.

Co-authorship network of co-authors of Daniel A. Henderson

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

Henderson, Daniel A.. (2024). Modelling and Analysis of Rank Ordered Data with Ties via a Generalized Plackett-Luce Model. Bayesian Analysis. 20(3). 1 indexed citations

Henderson, Daniel A., et al.. (2023). Multilevel emulation for stochastic computer models with application to large offshore wind farms. Journal of the Royal Statistical Society Series C (Applied Statistics). 72(3). 608–627. 2 indexed citations

Henderson, Daniel A., R. J. Boys, Carole J. Proctor, & Darren J. Wilkinson. (2018). Linking systems biology models to data: A stochastic kinetic model of p53 oscillations. Oxford University Press eBooks.

Henderson, Daniel A., et al.. (2017). A Comparison of Truncated and Time-Weighted Plackett–Luce Models for Probabilistic Forecasting of Formula One Results. Bayesian Analysis. 13(2). 11 indexed citations

Wilson, Kevin J., Daniel A. Henderson, & John Quigley. (2017). Emulation of Utility Functions Over a Set of Permutations: Sequencing Reliability Growth Tasks. Technometrics. 60(3). 273–285. 4 indexed citations

Sherlock, Chris, Andrew Golightly, & Daniel A. Henderson. (2016). Adaptive, Delayed-Acceptance MCMC for Targets With Expensive Likelihoods. Journal of Computational and Graphical Statistics. 26(2). 434–444. 22 indexed citations

Golightly, Andrew, Daniel A. Henderson, & Chris Sherlock. (2015). Efficient particle MCMC for exact inference in stochastic biochemical network models through approximation of expensive likelihoods. Lancaster EPrints (Lancaster University). 2 indexed citations

Henderson, Daniel A., Andrew W. Baggaley, Anvar Shukurov, et al.. (2014). Regional variations in the European Neolithic dispersal: the role of the coastlines. Antiquity. 88(342). 1291–1302. 13 indexed citations

Henderson, Daniel A., R. J. Boys, & Darren J. Wilkinson. (2009). Bayesian Calibration of a Stochastic Kinetic Computer Model Using Multiple Data Sources. Biometrics. 66(1). 249–256. 13 indexed citations

10.

Jones, M. C. & Daniel A. Henderson. (2009). Maximum likelihood kernel density estimation: On the potential of convolution sieves. Computational Statistics & Data Analysis. 53(10). 3726–3733. 16 indexed citations

11.

Sanda, P. N., et al.. (2008). F-T D IBM P6 M. IEEE Micro. 28(2). 30–38. 41 indexed citations

12.

Sanda, P. N., et al.. (2008). Fault-Tolerant Design of the IBM Power6 Microprocessor. IEEE Micro. 28(2). 30–38. 51 indexed citations

13.

Jones, M. C. & Daniel A. Henderson. (2007). Miscellanea Kernel-Type Density Estimation on the Unit Interval. Biometrika. 94(4). 977–984. 29 indexed citations

14.

Sanda, P. N., et al.. (2007). Fault - tolerant design of the IBM POWER6™ microprocessor. 1–10. 2 indexed citations

15.

Boys, Richard J., et al.. (2005). CaliBayes: Integration of GRID based simulation and data resources for Bayesian calibration of biological models. BMC Bioinformatics. 6(S3). 6 indexed citations

16.

Boys, Richard J. & Daniel A. Henderson. (2004). A Bayesian Approach to DNA Sequence Segmentation. Biometrics. 60(3). 573–581. 51 indexed citations

17.

Boys, R. J. & Daniel A. Henderson. (2002). On Determining the Order of Markov Dependence of an Observed Process Governed by a Hidden Markov Model. Scientific Programming. 10(3). 241–251. 18 indexed citations

18.

Boys, Richard J., Daniel A. Henderson, & Darren J. Wilkinson. (2000). Detecting Homogeneous Segments in DNA Sequences by Using Hidden Markov Models. Journal of the Royal Statistical Society Series C (Applied Statistics). 49(2). 269–285. 54 indexed citations

19.

Avery, Peter & Daniel A. Henderson. (1999). Fitting Markov Chain Models to Discrete State Series such as DNA Sequences. Journal of the Royal Statistical Society Series C (Applied Statistics). 48(1). 53–61. 31 indexed citations

20.

Avery, Peter & Daniel A. Henderson. (1999). Detecting a Changed Segment in DNA Sequences. Journal of the Royal Statistical Society Series C (Applied Statistics). 48(4). 489–503. 14 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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