Ray Chambers

1.9k total citations
71 papers, 1.2k citations indexed

About

Ray Chambers is a scholar working on Statistics and Probability, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Ray Chambers has authored 71 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Statistics and Probability, 25 papers in Management Science and Operations Research and 25 papers in Economics and Econometrics. Recurrent topics in Ray Chambers's work include Statistical Methods and Bayesian Inference (26 papers), Spatial and Panel Data Analysis (20 papers) and demographic modeling and climate adaptation (18 papers). Ray Chambers is often cited by papers focused on Statistical Methods and Bayesian Inference (26 papers), Spatial and Panel Data Analysis (20 papers) and demographic modeling and climate adaptation (18 papers). Ray Chambers collaborates with scholars based in Australia, United Kingdom and Italy. Ray Chambers's co-authors include Nikos Tzavidis, Jens Breckling, Nicola Salvati, Hukum Chandra, Robert G. Clark, Ayoub Saei, Stefano Marchetti, Monica Pratesi, Philip Kokic and Maria Giovanna Ranalli and has published in prestigious journals such as The Lancet, JAMA and SHILAP Revista de lepidopterología.

In The Last Decade

Ray Chambers

68 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ray Chambers Australia 18 569 464 389 202 122 71 1.2k
Robert E. Fay United States 11 686 1.2× 440 0.9× 296 0.8× 256 1.3× 116 1.0× 21 1.4k
Gauri Sankar Datta United States 24 1.1k 1.9× 546 1.2× 470 1.2× 108 0.5× 180 1.5× 74 1.7k
Partha Lahiri United States 19 878 1.5× 649 1.4× 607 1.6× 156 0.8× 211 1.7× 68 1.6k
Isabel Molina Spain 16 326 0.6× 564 1.2× 390 1.0× 303 1.5× 150 1.2× 49 1.1k
Monica Pratesi Italy 19 219 0.4× 358 0.8× 208 0.5× 179 0.9× 79 0.6× 69 982
Roger A. Herriot United States 4 443 0.8× 406 0.9× 276 0.7× 149 0.7× 111 0.9× 6 930
Rachel Harter United States 8 310 0.5× 248 0.5× 198 0.5× 116 0.6× 76 0.6× 14 666
Raymond L. Chambers Australia 19 697 1.2× 195 0.4× 210 0.5× 131 0.6× 29 0.2× 52 1.1k
Jean‐Claude Deville France 10 882 1.6× 295 0.6× 153 0.4× 353 1.7× 26 0.2× 23 1.7k
María José Lombardía Spain 14 263 0.5× 315 0.7× 225 0.6× 104 0.5× 90 0.7× 36 703

Countries citing papers authored by Ray Chambers

Since Specialization
Citations

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

Fields of papers citing papers by Ray Chambers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ray Chambers

This figure shows the co-authorship network connecting the top 25 collaborators of Ray Chambers. A scholar is included among the top collaborators of Ray Chambers 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 Ray Chambers. Ray Chambers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chambers, Ray, Setareh Ranjbar, Nicola Salvati, & Barbara Pacini. (2022). Weighting, Informativeness and Causal Inference, with an Application to Rainfall Enhancement. Journal of the Royal Statistical Society Series A (Statistics in Society). 185(4). 1584–1612. 1 indexed citations
2.
Chambers, Ray. (2020). Should the Census Have More Spine?. SHILAP Revista de lepidopterología. 1 indexed citations
3.
Chambers, Ray, et al.. (2019). Improved Secondary Analysis of Linked Data: A Framework and an Illustration. Journal of the Royal Statistical Society Series A (Statistics in Society). 183(1). 37–59. 15 indexed citations
4.
Chandra, Hukum, Nicola Salvati, & Ray Chambers. (2017). Small area prediction of counts under a non-stationary spatial model. Spatial Statistics. 20. 30–56. 25 indexed citations
5.
Schmid, Timo, Nikos Tzavidis, Ralf Münnich, & Ray Chambers. (2016). Outlier Robust Small‐Area Estimation Under Spatial Correlation. Scandinavian Journal of Statistics. 43(3). 806–826. 16 indexed citations
6.
Murray, Christopher & Ray Chambers. (2015). Keeping score: fostering accountability for children's lives. The Lancet. 386(9988). 3–5. 7 indexed citations
7.
Chambers, Ray, et al.. (2011). Accounting For Spatiotemporal Variation Of Rainfall Measurements When Evaluating Ground-based Methods Of Weather Modification. The Journal of Weather Modification. 43(1). 44–63. 9 indexed citations
8.
Chambers, Ray, et al.. (2010). Statistical Modeling of Rainfall Enhancement. The Journal of Weather Modification. 42(1). 13–32. 10 indexed citations
9.
Salvati, Nicola, Hukum Chandra, Maria Giovanna Ranalli, & Ray Chambers. (2010). Small area estimation using a nonparametric model-based direct estimator. Computational Statistics & Data Analysis. 54(9). 2159–2171. 25 indexed citations
10.
Salvati, Nicola, Monica Pratesi, Nikos Tzavidis, & Ray Chambers. (2009). SPATIAL M-QUANTILE MODELS FOR SMALL AREA ESTIMATION. Statistics in Transition New Series. 10(2). 251–267. 5 indexed citations
11.
Chandra, Hukum & Ray Chambers. (2009). Multipurpose weighting for small area estimation. Journal of Official Statistics. 25(3). 379–395. 13 indexed citations
12.
Chandra, Hukum & Ray Chambers. (2008). Multipurpose Small Area Estimation. Research Online (University of Wollongong). 2 indexed citations
13.
Chandra, Hukum, Nicola Salvati, & Ray Chambers. (2007). Small area estimation for spatially correlated populations - A comparison of direct and indirect model-based methods. Research Online (University of Wollongong). 8(17). 887–906. 26 indexed citations
14.
Laaksonen, Seppo & Ray Chambers. (2005). Survey estimation under informative nonresponse with follow-up. Quality Engineering. 22(1). 495–496. 1 indexed citations
15.
Chambers, Ray, et al.. (2004). Robust Automatic Methods for Outlier and Error Detection. Journal of the Royal Statistical Society Series A (Statistics in Society). 167(2). 323–339. 20 indexed citations
16.
Saei, Ayoub & Ray Chambers. (2003). Small Area Estimation Under Linear and GeneralizedLinear Mixed Models With Time and Area Effects. JAMA. 279(5). 347–8. 30 indexed citations
17.
Steele, Fiona, James Brown, & Ray Chambers. (2002). A controlled donor imputation system for a one-number census. Journal of the Royal Statistical Society Series A (Statistics in Society). 165(3). 495–522. 5 indexed citations
18.
Steele, Fiona, James Brown, & Ray Chambers. (2002). A Controlled Donor Imputation System for a One-Number Census. Journal of the Royal Statistical Society Series A (Statistics in Society). 165(3). 495–522. 8 indexed citations
19.
Chambers, Ray, et al.. (2001). EVALUATION OF SMALL AREA ESTIMATION METHODS - AN APPLICATION TO UNEMPLOYMENT ESTIMATES FROM THE UK LFS. 47 indexed citations
20.
Kokic, Philip, et al.. (1997). A Measure of Production Performance. Journal of Business and Economic Statistics. 15(4). 445–445. 9 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.

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