Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average within
it), or reaches the top citation threshold in at least one of its specific research topics.
Comparison Methods for Stochastic Models and Risks
2003984 citationsMark Anthony McCombTechnometricsprofile →
Countries citing papers authored by Mark Anthony McComb
Since Specialization
Citations
This map shows the geographic impact of Mark Anthony McComb'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 Mark Anthony McComb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Anthony McComb more than expected).
Fields of papers citing papers by Mark Anthony McComb
This network shows the impact of papers produced by Mark Anthony McComb. 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 Mark Anthony McComb. The network helps show where Mark Anthony McComb may publish in the future.
Mark Anthony McComb is a scholar working on Infectious Diseases, Organic Chemistry and Surgery, having authored 8 papers that have together received 1.3k indexed citations. The work is most often cited by research in Statistics and Probability (488 citations), Management Science and Operations Research (529 citations) and Finance (353 citations). Mark Anthony McComb has collaborated with scholars based in United States. Their work appears in journals such as Technometrics and Medical Entomology and Zoology.
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
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Rankless may not fully capture the entirety of a scholar's output or impact.