Derek S. Young

2.3k total citations · 1 hit paper
49 papers, 1.5k citations indexed

About

Derek S. Young is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Derek S. Young has authored 49 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Statistics and Probability, 17 papers in Artificial Intelligence and 6 papers in Statistics, Probability and Uncertainty. Recurrent topics in Derek S. Young's work include Statistical Methods and Bayesian Inference (16 papers), Bayesian Methods and Mixture Models (15 papers) and Advanced Statistical Methods and Models (15 papers). Derek S. Young is often cited by papers focused on Statistical Methods and Bayesian Inference (16 papers), Bayesian Methods and Mixture Models (15 papers) and Advanced Statistical Methods and Models (15 papers). Derek S. Young collaborates with scholars based in United States, Iran and France. Derek S. Young's co-authors include David R. Hunter, Didier Chauveau, Tatiana Benaglia, Thomas Mathew, David J. Hunter, Kimberly F. Sellers, Yixuan Zou, Xi Chen, Ricardo Nilo‐Poyanco and Hilal Özcebe and has published in prestigious journals such as SHILAP Revista de lepidopterología, Statistics in Medicine and Journal of Statistical Software.

In The Last Decade

Derek S. Young

45 papers receiving 1.5k citations

Hit Papers

mixtools: AnRPackage for Analyzing Finite Mixture Models 2009 2026 2014 2020 2009 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Derek S. Young United States 14 322 270 259 171 133 49 1.5k
Dirk Eddelbuettel United States 13 422 1.3× 496 1.8× 305 1.2× 148 0.9× 125 0.9× 33 1.9k
Didier Chauveau France 14 331 1.0× 403 1.5× 322 1.2× 170 1.0× 130 1.0× 30 1.5k
Benjamin Hofner Germany 25 413 1.3× 228 0.8× 357 1.4× 192 1.1× 82 0.6× 59 1.8k
Gerhard Tutz Germany 9 417 1.3× 372 1.4× 162 0.6× 216 1.3× 203 1.5× 16 2.6k
Fabian Scheipl Germany 19 497 1.5× 198 0.7× 163 0.6× 119 0.7× 108 0.8× 43 1.8k
Romain François France 5 205 0.6× 235 0.9× 185 0.7× 112 0.7× 66 0.5× 11 1.2k
Anja Struyf Belgium 21 356 1.1× 187 0.7× 123 0.5× 115 0.7× 128 1.0× 26 1.9k
Rebecca Killick United Kingdom 15 273 0.8× 369 1.4× 215 0.8× 308 1.8× 501 3.8× 60 2.9k
Benoît Liquet France 18 265 0.8× 171 0.6× 190 0.7× 127 0.7× 106 0.8× 90 1.4k
Andrew A. Neath United States 11 550 1.7× 392 1.5× 125 0.5× 86 0.5× 125 0.9× 31 2.3k

Countries citing papers authored by Derek S. Young

Since Specialization
Citations

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

Fields of papers citing papers by Derek S. Young

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Derek S. Young. 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 Derek S. Young. The network helps show where Derek S. Young may publish in the future.

Co-authorship network of co-authors of Derek S. Young

This figure shows the co-authorship network connecting the top 25 collaborators of Derek S. Young. A scholar is included among the top collaborators of Derek S. Young 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 Derek S. Young. Derek S. Young 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.
Young, Derek S., et al.. (2025). Some estimation and inference considerations for the zero-inflated discrete Weibull distribution. Communications in Statistics - Simulation and Computation. 1–22.
2.
Amrhein, Timothy J., et al.. (2024). Diagnostic Yield of Decubitus CT Myelography for Detection of CSF-Venous Fistulas. American Journal of Neuroradiology. 45(10). 1597–1604. 8 indexed citations
3.
Young, Derek S., et al.. (2023). Approximate tolerance intervals for nonparametric regression models. Journal of nonparametric statistics. 36(1). 212–239.
4.
Parker, Peter A., et al.. (2023). An empirical semiparametric one‐sided confidence bound for lower quantiles of distributions with positive support. Quality and Reliability Engineering International. 40(4). 1618–1635. 1 indexed citations
5.
Young, Derek S., et al.. (2023). Finite mixtures of mean-parameterized Conway–Maxwell–Poisson models. Statistical Papers. 65(3). 1469–1492. 1 indexed citations
6.
Lamarche, Carlos, et al.. (2021). Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data. Econometrics and Statistics. 31. 49–65.
7.
Ünlü, Hande Konşuk, et al.. (2020). A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey. Journal of Applied Statistics. 49(4). 1003–1017. 10 indexed citations
8.
Zou, Yixuan & Derek S. Young. (2020). Improving coverage probabilities for parametric tolerance intervals via bootstrap calibration. Statistics in Medicine. 39(16). 2152–2166. 6 indexed citations
9.
Young, Derek S., et al.. (2020). Zero‐inflatedmodeling partII:Zero‐inflatedmodels for complex data structures. Wiley Interdisciplinary Reviews Computational Statistics. 14(2). 5 indexed citations
10.
Young, Derek S., et al.. (2020). Zero‐inflatedmodeling part I: Traditionalzero‐inflatedcount regression models, their applications, and computational tools. Wiley Interdisciplinary Reviews Computational Statistics. 14(1). 17 indexed citations
11.
Young, Derek S., et al.. (2019). Influence of Adrenal Venous Sampling on Management in Patients with Primary Aldosteronism Independent of Lateralization on Cross-Sectional Imaging. Journal of the American College of Surgeons. 229(1). 116–124. 12 indexed citations
12.
Young, Derek S., et al.. (2019). Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering. Advances in Data Analysis and Classification. 13(4). 1053–1082. 16 indexed citations
13.
Young, Derek S., et al.. (2018). Approximate confidence and tolerance limits for the discrete Pareto distribution for characterizing extremes in count data. Statistica Neerlandica. 73(1). 4–21. 1 indexed citations
14.
Mitelman, Serge A., Monte S. Buchsbaum, Derek S. Young, et al.. (2017). Increased white matter metabolic rates in autism spectrum disorder and schizophrenia. Brain Imaging and Behavior. 12(5). 1290–1305. 18 indexed citations
15.
Young, Derek S.. (2014). Tolerance Intervals for Hypergeometric and Negative Hypergeometric Variables. Sankhya B. 77(1). 114–140. 4 indexed citations
16.
Young, Derek S., et al.. (2014). Choosing a coverage probability for forecasting the incidence of cancer. Statistics in Medicine. 33(23). 4104–4115. 2 indexed citations
17.
Hunter, David R. & Derek S. Young. (2011). Semiparametric mixtures of regressions. Journal of nonparametric statistics. 24(1). 19–38. 42 indexed citations
18.
Young, Derek S.. (2010). tolerance: An R Package for Estimating Tolerance Intervals. SHILAP Revista de lepidopterología. 11 indexed citations
19.
Benaglia, Tatiana, Didier Chauveau, David R. Hunter, & Derek S. Young. (2009). mixtools: An R Package for Analyzing Mixture Models. SHILAP Revista de lepidopterología. 45 indexed citations
20.
Benaglia, Tatiana, Didier Chauveau, David J. Hunter, & Derek S. Young. (2009). mixtools: An R package for analyzing finite mixture models. HAL (Le Centre pour la Communication Scientifique Directe). 31 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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