Jeremy York

10.8k citations
6 papers · 7.2k indexed · 3 hit papers · h-index 5

Jeremy York

6 papers receiving 6.7k citations

Hit Papers

Amazon.com recommendations: item-to-item collaborative fi...3.4k199120262002201410002.0k3.0k

Peers

Jeremy York
Comparison fields: 5 of 185
  • Information Systems 2.8k
  • Statistics and Probability 903
  • Transportation 440
  • Artificial Intelligence 1.8k
  • Management Science and Operations Research 689
Replace Mark S. Handcock with:
Mark S. Handcock United States
Kevin Lang United States
Stephen E. Fienberg United States
MTW United States
Anirban Dasgupta United States
David Spiegelhalter United Kingdom
Helmut Lütkepohl Germany
Xiao‐Jun Zeng United Kingdom
Shu‐Kay Ng Australia
David R. Hunter United States
Jeremy York relative to Mark S. Handcock United States Mark S. Handcock's profile →
Citations per field
00.5×9.0×
Mark S. Handcock · 1×
Citations per year

Countries citing papers authored by Jeremy York

Since Specialization
Citations

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

Fields of papers citing papers by Jeremy York

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeremy York. 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 Jeremy York. The network helps show where Jeremy York may publish in the future.

Co-authorship network

The 7 scholars most cited alongside Jeremy York, 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 Jeremy York Line = papers co-authored together Jeremy York links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1
Amazon.com recommendations: item-to-item collaborative filteringbreakdown →
20033434
2
Bayesian Graphical Models for Discrete Databreakdown →
1995683
3 199536
4 199220
5 19921
6
Bayesian image restoration, with two applications in spatial statisticsbreakdown →
19912993

About Jeremy York

Jeremy York is a scholar working on Statistics and Probability, Marketing, Signal Processing, Environmental Engineering and Artificial Intelligence, having authored 6 papers that have together received 7.2k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (2 papers), Statistical Methods and Inference (2 papers), Bayesian Modeling and Causal Inference (2 papers), Recommender Systems and Techniques (1 paper), Colorectal Cancer Screening and Detection (1 paper), Bayesian Methods and Mixture Models (1 paper), Statistical Distribution Estimation and Applications (1 paper) and Data Management and Algorithms (1 paper). The work is most often cited by research in Information Systems (2.8k citations), Statistics and Probability (903 citations), Transportation (440 citations), Artificial Intelligence (1.8k citations) and Management Science and Operations Research (689 citations). Jeremy York has collaborated with scholars based in United States, Norway and France. Frequent co-authors include Brent Smith, Greg Linden, Julian Besag, David Madigan, Denis Allard, Ivar Heuch and Rolv T. Lie. Their work appears in journals such as Artificial Intelligence, Annals of the Institute of Statistical Mathematics, IEEE Internet Computing, International Statistical Review and Journal of the Royal Statistical Society Series C (Applied Statistics).

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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