Ian T. Jolliffe

122 papers receiving 31.3k citations

Ian T. Jolliffe's Hit Papers

Principal component analysis: a review and recent developments 2016 · 6.7k citations
6.7k0+18+36Years since publication2.0k4.0k6.0k

Peers

Ian T. Jolliffe
Comparison fields: 5 of 241
  • Analytical Chemistry 2.8k
  • Computational Mathematics 160
  • Signal Processing 2.6k
  • Computer Vision and Pattern Recognition 5.0k
  • Statistics and Probability 1.9k
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Countries citing papers authored by Ian T. Jolliffe

Since Specialization
Citations

This map shows the geographic impact of Ian T. Jolliffe'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 Ian T. Jolliffe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian T. Jolliffe more than expected).

Fields of papers citing papers by Ian T. Jolliffe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian T. Jolliffe. 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 Ian T. Jolliffe. The network helps show where Ian T. Jolliffe may publish in the future.

Co-authors

The 25 scholars most cited alongside Ian T. Jolliffe, 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 Ian T. Jolliffe Line = papers co-authored together Ian T. Jolliffe links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 128 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Principal Component Analysis
Hit paper breakdown →
19887363
2
Principal Component Analysis
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19866746
3
Principal component analysis: a review and recent developments
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20166668
4
Theory and Applications of Correspondence Analysis.
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19862418
5
Introduction to Multiple Time Series Analysis
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19931366
6
Empirical orthogonal functions and related techniques in atmospheric science: A review
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2007952
7
Principal Component Analysis and Factor Analysis
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1986834
8
Discarding Variables in a Principal Component Analysis. I: Artificial Data
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1972717
9
A Note on the Use of Principal Components in Regression
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1982632
10 2003564
11 1997301
12 1973297
13 2003259
14 1995251
15 1986218
16 1990193
17 1988187
18 1996179
19 1997178
20 1986172

About Ian T. Jolliffe

Ian T. Jolliffe is a scholar working on Statistics and Probability, Analytical Chemistry, Global and Planetary Change, Atmospheric Science and Management Science and Operations Research, having authored 128 papers that have together received 33.1k indexed citations. Recurring topics across this work include Climate variability and models (21 papers), Spectroscopy and Chemometric Analyses (20 papers), Advanced Statistical Methods and Models (16 papers), Meteorological Phenomena and Simulations (13 papers), Sensory Analysis and Statistical Methods (10 papers), Forecasting Techniques and Applications (10 papers), Hydrology and Drought Analysis (7 papers) and Statistical Methods and Applications (7 papers). The work is most often cited by research in Analytical Chemistry (2.8k citations), Computational Mathematics (160 citations), Signal Processing (2.6k citations), Computer Vision and Pattern Recognition (5.0k citations) and Statistics and Probability (1.9k citations). Ian T. Jolliffe has collaborated with scholars based in United Kingdom, Slovakia and United States. Frequent co-authors include Colin R. Goodall, Jorge F. C. L. Cadima, Michael John Greenacre, Helmut Lütkepohl, David B. Stephenson, Abdel Hannachi, Nickolay T. Trendafilov, Mudassir Uddin, Michael A. A. Cox and Trevor F. Cox. Their work appears in journals such as International Journal of Climatology, Journal of the Royal Statistical Society Series C (Applied Statistics), Technometrics, Weather and Forecasting and Biometrics.

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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