John J. Peterson

2.8k total citations · 1 hit paper
52 papers, 2.2k citations indexed

About

John J. Peterson is a scholar working on Management Science and Operations Research, Statistics, Probability and Uncertainty and Statistics and Probability. According to data from OpenAlex, John J. Peterson has authored 52 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Management Science and Operations Research, 16 papers in Statistics, Probability and Uncertainty and 13 papers in Statistics and Probability. Recurrent topics in John J. Peterson's work include Optimal Experimental Design Methods (24 papers), Statistical Methods in Clinical Trials (9 papers) and Advanced Multi-Objective Optimization Algorithms (9 papers). John J. Peterson is often cited by papers focused on Optimal Experimental Design Methods (24 papers), Statistical Methods in Clinical Trials (9 papers) and Advanced Multi-Objective Optimization Algorithms (9 papers). John J. Peterson collaborates with scholars based in United States, United Kingdom and Japan. John J. Peterson's co-authors include A. Colin Cameron, Pravin K. Trivedi, Enrique Del Castillo, Scott Temple, Laura Ación, Stephan Arndt, Steven Novick, Roy S. Weiner, Johnson Y.N. Lau and Rajesh K. Davda and has published in prestigious journals such as Blood, The Journal of Immunology and Technometrics.

In The Last Decade

John J. Peterson

51 papers receiving 2.1k citations

Hit Papers

Regression Analysis of Count Data 1999 2026 2008 2017 1999 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John J. Peterson United States 20 383 308 279 245 241 52 2.2k
Peter Hackl Austria 19 506 1.3× 212 0.7× 306 1.1× 422 1.7× 264 1.1× 50 2.2k
Ron S. Kenett Italy 28 415 1.1× 110 0.4× 530 1.9× 420 1.7× 123 0.5× 183 2.9k
Youngjo Lee South Korea 29 383 1.0× 419 1.4× 202 0.7× 1.3k 5.2× 73 0.3× 192 3.6k
Louis Anthony Cox United States 26 180 0.5× 192 0.6× 370 1.3× 139 0.6× 82 0.3× 163 2.3k
Jong‐Min Kim South Korea 26 196 0.5× 389 1.3× 134 0.5× 895 3.7× 97 0.4× 303 3.1k
Ralph C. St. John United States 10 308 0.8× 102 0.3× 145 0.5× 159 0.6× 166 0.7× 17 2.2k
Karen A. F. Copeland United States 14 285 0.7× 244 0.8× 227 0.8× 669 2.7× 121 0.5× 28 2.9k
Kuldeep Kumar Australia 24 310 0.8× 332 1.1× 60 0.2× 221 0.9× 66 0.3× 179 2.6k
Peter Winker Germany 24 903 2.4× 662 2.1× 354 1.3× 266 1.1× 540 2.2× 117 2.8k
Christopher J. Nachtsheim United States 29 1.3k 3.4× 195 0.6× 490 1.8× 723 3.0× 880 3.7× 70 3.6k

Countries citing papers authored by John J. Peterson

Since Specialization
Citations

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

Fields of papers citing papers by John J. Peterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John J. Peterson. 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 John J. Peterson. The network helps show where John J. Peterson may publish in the future.

Co-authorship network of co-authors of John J. Peterson

This figure shows the co-authorship network connecting the top 25 collaborators of John J. Peterson. A scholar is included among the top collaborators of John J. Peterson 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 John J. Peterson. John J. Peterson 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.
Campbell, John M., Kevin L. Facchine, Benoît Igne, et al.. (2019). Analysis of unstable degradation impurities of a benzodiazepine and their quantification without isolation using multiple linear regression. Journal of Pharmaceutical and Biomedical Analysis. 167. 1–6. 5 indexed citations
2.
Hodnett, Neil S., et al.. (2016). Kinetic Model Development for Accelerated Stability Studies. AAPS PharmSciTech. 18(4). 1158–1176. 28 indexed citations
3.
Novick, Steven, et al.. (2014). Dissolution Curve Comparisons Through theF2Parameter, a Bayesian Extension of thef2Statistic. Journal of Biopharmaceutical Statistics. 25(2). 351–371. 10 indexed citations
4.
Mockus, Linas, John J. Peterson, José M. Laínez‐Aguirre, & Gintaras V. Reklaitis. (2014). Batch-to-Batch Variation: A Key Component for Modeling Chemical Manufacturing Processes. Organic Process Research & Development. 19(8). 908–914. 28 indexed citations
5.
Liu, Li, Hong Shi, Yuan Liu, et al.. (2011). Synergistic Effects of Foretinib with HER-Targeted Agents in MET and HER1- or HER2-Coactivated Tumor Cells. Molecular Cancer Therapeutics. 10(3). 518–530. 64 indexed citations
6.
Novick, Steven, Karen Chiswell, & John J. Peterson. (2011). A Bayesian Approach to Show Assay Equivalence with Replicate Measurements Over a Specified Response Range. Statistics in Biopharmaceutical Research. 4(2). 102–117. 3 indexed citations
7.
Castagnoli, C., et al.. (2010). Application of Quality by Design Principles for the Definition of a Robust Crystallization Process for Casopitant Mesylate. Organic Process Research & Development. 14(6). 1407–1419. 27 indexed citations
9.
Peterson, John J.. (2009). A review of synergy concepts of nonlinear blending and dose-reduction profiles. Frontiers in Bioscience-Scholar. S2(2). 483–503. 6 indexed citations
10.
Boye, Sanford L., John J. Peterson, Hilda Petrs‐Silva, et al.. (2006). Transduction and Tropism of an Abbreviated Form of CMV–Chicken ß–Actin Promoter (CBA) With AAV in Mouse Retina. Investigative Ophthalmology & Visual Science. 47(13). 852–852. 1 indexed citations
11.
Ación, Laura, John J. Peterson, Scott Temple, & Stephan Arndt. (2006). Authors' reply. Statistics in Medicine. 25(22). 3946–3948. 3 indexed citations
12.
Ación, Laura, John J. Peterson, Scott Temple, & Stephan Arndt. (2005). Probabilistic index: an intuitive non‐parametric approach to measuring the size of treatment effects. Statistics in Medicine. 25(4). 591–602. 187 indexed citations
13.
Castillo, Enrique Del, et al.. (2005). Model and Distribution-Robust Process Optimization with Noise Factors. Journal of Quality Technology. 37(3). 210–222. 18 indexed citations
14.
Castillo, Enrique Del, et al.. (2004). A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables. Journal of Applied Statistics. 31(3). 251–270. 64 indexed citations
16.
Peterson, John J., Suntara Cahya, & Enrique Del Castillo. (2002). A General Approach to Confidence Regions for Optimal Factor Levels of Response Surfaces. Biometrics. 58(2). 422–431. 24 indexed citations
17.
18.
Peterson, John J., et al.. (1998). Clearance of Murine Leukaemia Virus from Monoclonal Antibody Solution by a Hydrophilic PVDF Microporous Membrane Filter. Biologicals. 26(2). 167–172. 10 indexed citations
19.
Peterson, John J.. (1993). A General Approach to Ridge Analysis With Confidence Intervals. Technometrics. 35(2). 204–214. 25 indexed citations
20.
Peterson, John J.. (1986). A note on some model selection criteria. Statistics & Probability Letters. 4(5). 227–230. 1 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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