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.
Randomized Quantile Residuals
1996702 citationsPeter K. Dunn, Gordon K. SmythJournal of Computational and Graphical Statisticsprofile →
Generalized Linear Models With Examples in R
2018257 citationsPeter K. Dunn, Gordon K. Smythprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Peter K. Dunn'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 Peter K. Dunn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter K. Dunn more than expected).
This network shows the impact of papers produced by Peter K. Dunn. 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 Peter K. Dunn. The network helps show where Peter K. Dunn may publish in the future.
Co-authorship network of co-authors of Peter K. Dunn
This figure shows the co-authorship network connecting the top 25 collaborators of Peter K. Dunn.
A scholar is included among the top collaborators of Peter K. Dunn 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 Peter K. Dunn. Peter K. Dunn is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Marshman, Margaret, et al.. (2015). A Case Study of the Attitudes and Preparedness of a Group of Secondary Mathematics Teachers towards Statistics.. USC Research Bank (University of the Sunshine Coast). 29(1). 51–64.5 indexed citations
Richardson, Alice, et al.. (2013). The impact of tutor, extract and word on the correct definition of lexically ambiguous words in statistics. University of Canberra Research Portal. 185–192.3 indexed citations
12.
Carey, Michael, Robert Mannell, & Peter K. Dunn. (2011). Does a rater’s familiarity with a candidate’s pronunciation affect the rating in oral proficiency interviews?. USC Research Bank (University of the Sunshine Coast).51 indexed citations
Dunn, Peter K., et al.. (2009). Microsimulation Model Design in Lower Manhattan: A Street Management Approach. Transportation Research Board 88th Annual MeetingTransportation Research Board.2 indexed citations
16.
Dunn, Peter K.. (2008). Building-In Quality Rather Than Assessing Quality Afterwards: A Technological Solution to Ensuring Computational Accuracy in Learning Materials. University of Southern Queensland ePrints (University of Southern Queensland). 27(1). 47–63.1 indexed citations
17.
Dunn, Peter K.. (2003). Understanding statistics using computer demonstrations. University of Southern Queensland ePrints (University of Southern Queensland). 22(3). 83–103.4 indexed citations
18.
Dunn, Peter K. & Gordon K. Smyth. (2001). Tweedie Family Densities: Methods of Evaluation. USC Research Bank (University of the Sunshine Coast).5 indexed citations
Dunn, Peter K. & Gordon K. Smyth. (1996). Randomized Quantile Residuals. Journal of Computational and Graphical Statistics. 5(3). 236–244.702 indexed citations breakdown →
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.