Brian D. Marx

7.5k citations
82 papers · 5.2k indexed · 1 hit paper · h-index 29

Impact in

Papers in

Brian D. Marx

77 papers receiving 4.9k citations

Hit Papers

Flexible smoothing with B-splines and penalties 1996 · 2.6k citations
2.6k199620262006201650010001.5k2.0k2.5k

Peers

Brian D. Marx
Comparison fields: 5 of 206
  • Statistics and Probability 1.7k
  • Analytical Chemistry 332
  • Statistics, Probability and Uncertainty 214
  • Environmental Engineering 344
  • Earth-Surface Processes 161
Replace Richard A. Davis with:
Richard A. Davis United States
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Citations per field
00.5×1.5×
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Citations per year

Countries citing papers authored by Brian D. Marx

Since Specialization
Citations

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

Fields of papers citing papers by Brian D. Marx

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian D. Marx. 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 Brian D. Marx. The network helps show where Brian D. Marx may publish in the future.

Co-authors

The 25 scholars most cited alongside Brian D. Marx, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian D. Marx Line = papers co-authored together Brian D. Marx links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202120
2 20217
3 20204
4 202017
5 201936
6 20177
7 201752
8 201663
9 20156
10 20141
11
Improving Engineering Students' Cognitive and Affective Preparedness with a Pre-Instructional E-Learning Strategy
20107
12 20103
13 200910
14 20094
15 200832
16
Comparison of Greedy Strategies for Learning Markov Networks of Treewidth k.
20074
17 200642
18 200526
19
Flexible smoothing with B-splines and penalties
Hit paper breakdown →
19962556
20 199664

About Brian D. Marx

Brian D. Marx is a scholar working on Computational Mathematics, Statistics and Probability, Chemical Health and Safety, Radiological and Ultrasound Technology and Analytical Chemistry, having authored 82 papers that have together received 5.2k indexed citations. Recurring topics across this work include Advanced Statistical Methods and Models (23 papers), Statistical Methods and Inference (18 papers), Spectroscopy and Chemometric Analyses (8 papers), Statistical and numerical algorithms (7 papers), Coastal wetland ecosystem dynamics (7 papers), Occupational Health and Safety Research (5 papers), Diet and metabolism studies (4 papers) and Soil and Water Nutrient Dynamics (4 papers). The work is most often cited by research in Statistics and Probability (1.7k citations), Analytical Chemistry (332 citations), Statistics, Probability and Uncertainty (214 citations), Environmental Engineering (344 citations) and Earth-Surface Processes (161 citations). Brian D. Marx has collaborated with scholars based in United States, Netherlands and Germany. Frequent co-authors include Paul H.C. Eilers, J. Edward Jackson, John W. Day, Robert R. Lane, Edward Castañeda‐Moya, Víctor H. Rivera‐Monroy, Emily Hyfield, Emmanuel Lesaffre, Enrique Reyes and Jason N. Day. Their work appears in journals such as Technometrics, Chemometrics and Intelligent Laboratory Systems, Journal of Statistical Computation and Simulation, Journal of Computational and Graphical Statistics and Journal of Informetrics.

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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