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.
The Statistical Theory of Linear Systems
1990499 citationsJonathan D. Cryer et al.profile →
Time Series Analysis: With Applications in R
2010468 citationsJonathan D. Cryer, Kung‐Sik ChanCERN Document Server (European Organization for Nuclear Research)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jonathan D. Cryer
Since
Specialization
Citations
This map shows the geographic impact of Jonathan D. Cryer'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 Jonathan D. Cryer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan D. Cryer more than expected).
Fields of papers citing papers by Jonathan D. Cryer
This network shows the impact of papers produced by Jonathan D. Cryer. 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 Jonathan D. Cryer. The network helps show where Jonathan D. Cryer may publish in the future.
Co-authorship network of co-authors of Jonathan D. Cryer
This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan D. Cryer.
A scholar is included among the top collaborators of Jonathan D. Cryer 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 Jonathan D. Cryer. Jonathan D. Cryer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cryer, Jonathan D. & Kung‐Sik Chan. (2010). Time Series Analysis: With Applications in R. CERN Document Server (European Organization for Nuclear Research).468 indexed citations breakdown →
Cryer, Jonathan D., George W. Cobb, & Jeffrey A. Witmer. (1997). An electronic companion to business statistics. Medical Entomology and Zoology.1 indexed citations
9.
Cryer, Jonathan D. & Thomas P. Ryan. (1991). The estimation of sigma for an X chart: MR/d2 or S/c4?. Quality Engineering. 36(3). 157–158.11 indexed citations
10.
Cryer, Jonathan D., John C. Nankervis, & N. E. Savin. (1990). Forecast Error Symmetry in ARIMA Models. Journal of the American Statistical Association. 85(411). 724–724.1 indexed citations
Watson, Mark W. & Jonathan D. Cryer. (1987). Time Series Analysis.. Journal of the American Statistical Association. 82(400). 1195–1195.
13.
Cryer, Jonathan D., Julius S. Bendat, & Allan G. Piersol. (1987). Random Data.. Journal of the American Statistical Association. 82(400). 1197–1197.1 indexed citations
14.
Kyte, Michael, et al.. (1985). DEVELOPMENT AND APPLICATION OF TIME-SERIES TRANSIT RIDERSHIP MODELS FOR PORTLAND, OREGON. Transportation Research Record Journal of the Transportation Research Board. 9–18.3 indexed citations
15.
Ryan, Barbara F., Brian L. Joiner, & Jonathan D. Cryer. (1985). Minitab handbook : updated for release 16. Medical Entomology and Zoology.55 indexed citations
Cryer, Jonathan D., et al.. (1972). Monotone Median Regression. The Annals of Mathematical Statistics. 43(5). 1459–1469.24 indexed citations
19.
Cryer, Jonathan D. & M. R. Leadbetter. (1965). The variance of the number of zeros of stationary normal processes. NASA Technical Reports Server (NASA).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.