Iain L. MacDonald

2.4k total citations · 1 hit paper
26 papers, 1.7k citations indexed

About

Iain L. MacDonald is a scholar working on Statistics and Probability, Artificial Intelligence and Finance. According to data from OpenAlex, Iain L. MacDonald has authored 26 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Statistics and Probability, 7 papers in Artificial Intelligence and 3 papers in Finance. Recurrent topics in Iain L. MacDonald's work include Bayesian Methods and Mixture Models (5 papers), Statistical Methods and Bayesian Inference (4 papers) and Statistical Distribution Estimation and Applications (4 papers). Iain L. MacDonald is often cited by papers focused on Bayesian Methods and Mixture Models (5 papers), Statistical Methods and Bayesian Inference (4 papers) and Statistical Distribution Estimation and Applications (4 papers). Iain L. MacDonald collaborates with scholars based in South Africa, Germany and United Kingdom. Iain L. MacDonald's co-authors include Walter Zucchini, Roland Langrock, Shane P. Pederson, Simon Folkard, Philip Tucker, David Raubenheimer, Leonard Lerer, Melvin Varughese, Robert Kozak and David H. Cohen and has published in prestigious journals such as The Lancet, Technometrics and Biometrics.

In The Last Decade

Iain L. MacDonald

26 papers receiving 1.6k citations

Hit Papers

Hidden Markov Models for ... 2009 2026 2014 2020 2009 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Iain L. MacDonald South Africa 12 441 345 282 225 159 26 1.7k
Dirk Eddelbuettel United States 13 496 1.1× 422 1.2× 148 0.5× 70 0.3× 201 1.3× 33 1.9k
Gerhard Tutz Germany 9 372 0.8× 417 1.2× 216 0.8× 43 0.2× 185 1.2× 16 2.6k
Andrew A. Neath United States 11 392 0.9× 550 1.6× 86 0.3× 83 0.4× 177 1.1× 31 2.3k
Scott D. Grimshaw United States 14 311 0.7× 394 1.1× 72 0.3× 219 1.0× 240 1.5× 33 2.2k
Anders Brix United Kingdom 14 416 0.9× 536 1.6× 164 0.6× 148 0.7× 395 2.5× 23 2.8k
S. Fotopoulos United States 22 442 1.0× 397 1.2× 249 0.9× 174 0.8× 132 0.8× 123 2.2k
Chris P. Tsokos United States 20 345 0.8× 644 1.9× 165 0.6× 207 0.9× 108 0.7× 201 2.6k
D. J. Best Australia 21 460 1.0× 1.1k 3.1× 104 0.4× 169 0.8× 138 0.9× 120 2.9k
Anja Struyf Belgium 21 187 0.4× 356 1.0× 115 0.4× 166 0.7× 323 2.0× 26 1.9k
Huiyan Sang United States 19 387 0.9× 329 1.0× 231 0.8× 128 0.6× 506 3.2× 54 2.1k

Countries citing papers authored by Iain L. MacDonald

Since Specialization
Citations

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

Fields of papers citing papers by Iain L. MacDonald

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Iain L. MacDonald. 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 Iain L. MacDonald. The network helps show where Iain L. MacDonald may publish in the future.

Co-authorship network of co-authors of Iain L. MacDonald

This figure shows the co-authorship network connecting the top 25 collaborators of Iain L. MacDonald. A scholar is included among the top collaborators of Iain L. MacDonald 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 Iain L. MacDonald. Iain L. MacDonald 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.
MacDonald, Iain L., et al.. (2023). Continuous-time Markov-chain models for reaction systems: fast and slow processes. Reaction Kinetics Mechanisms and Catalysis. 136(4). 1757–1773. 2 indexed citations
2.
MacDonald, Iain L.. (2023). Simple examples of continuous-time Markov-chain models for reactions. Reaction Kinetics Mechanisms and Catalysis. 136(1). 1–11. 1 indexed citations
3.
MacDonald, Iain L.. (2021). Is EM really necessary here? Examples where it seems simpler not to use EM. AStA Advances in Statistical Analysis. 105(4). 629–647. 4 indexed citations
4.
MacDonald, Iain L., et al.. (2020). Fitting a reversible Markov chain by maximum likelihood: Converting an awkwardly constrained optimization problem to an unconstrained one. Physica A Statistical Mechanics and its Applications. 561. 125182–125182. 1 indexed citations
5.
MacDonald, Iain L.. (2020). Rejoinder: Fitting a folded normal distribution without EM. The Annals of Applied Statistics. 14(4). 1 indexed citations
6.
MacDonald, Iain L.. (2019). A coarse-grained Markov chain is a hidden Markov model. Physica A Statistical Mechanics and its Applications. 541. 123661–123661. 2 indexed citations
7.
MacDonald, Iain L., et al.. (2018). A Time-Series Model for Underdispersed or Overdispersed Counts. The American Statistician. 74(4). 317–328. 8 indexed citations
8.
Varughese, Melvin, et al.. (2017). Sequential quantiles via Hermite series density estimation. Electronic Journal of Statistics. 11(1). 9 indexed citations
9.
MacDonald, Iain L., et al.. (2015). A simple route to maximum-likelihood estimates of two-locus recombination fractions under inequality restrictions. Journal of Genetics. 94(3). 479–481. 1 indexed citations
10.
MacDonald, Iain L., et al.. (2015). Even More Direct Calculation of the Variance of a Maximum Penalized-Likelihood Estimator. The American Statistician. 70(1). 114–118. 1 indexed citations
11.
MacDonald, Iain L., et al.. (2014). Comments : EM-based likelihood inference for some lifetime distributions based on left truncated and right censored data and associated model discrimination. 48(2). 187–190. 1 indexed citations
12.
MacDonald, Iain L.. (2014). Does Newton–Raphson really fail?. Statistical Methods in Medical Research. 23(3). 308–311. 25 indexed citations
13.
Kruger, Ryan G., et al.. (2012). Nonlinear serial dependence in share returns on the Johannesburg Stock Exchange. RePEc: Research Papers in Economics. 14(2). 64–84. 2 indexed citations
14.
Zucchini, Walter & Iain L. MacDonald. (2009). Hidden Markov Models for Time Series. 316 indexed citations
15.
Zucchini, Walter & Iain L. MacDonald. (2009). Hidden Markov Models for Time Series: An Introduction Using R. TU Digital Collections (Thammasat University). 474 indexed citations breakdown →
16.
Zucchini, Walter, David Raubenheimer, & Iain L. MacDonald. (2007). Modeling Time Series of Animal Behavior by Means of a Latent‐State Model with Feedback. Biometrics. 64(3). 807–815. 41 indexed citations
17.
Tucker, Philip, Simon Folkard, & Iain L. MacDonald. (2003). Rest breaks and accident risk. The Lancet. 361(9358). 680–680. 128 indexed citations
18.
Cohen, David H., Iain L. MacDonald, & Robert Kozak. (2001). THE JAPANESE DISTRIBUTION SYSTEM FOR FINISHED BUILDING PRODUCTS: IN TRANSITION. The Pediatric Infectious Disease Journal. 20(5). 546–7. 2 indexed citations
19.
MacDonald, Iain L. & David Raubenheimer. (1995). Hidden Markov Models and Animal Behaviour. Biometrical Journal. 37(6). 701–712. 27 indexed citations
20.
MacDonald, Iain L. & Leonard Lerer. (1994). A Time-Series Analysis of Trends in Firearm-Related Homicide and Suicide. International Journal of Epidemiology. 23(1). 66–72. 11 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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