Alan E. Gelfand

306 papers receiving 22.7k citations

Alan E. Gelfand's Hit Papers

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets 2015 · 356 citations
3560+12+24Years since publication10002.0k3.0k4.0k

Peers

Alan E. Gelfand
Comparison fields: 5 of 216
  • Statistics and Probability 8.3k
  • Ecological Modeling 1.1k
  • Environmental Engineering 3.7k
  • Economics and Econometrics 5.3k
  • Artificial Intelligence 6.0k
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Countries citing papers authored by Alan E. Gelfand

Since Specialization
Citations

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

Fields of papers citing papers by Alan E. Gelfand

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alan E. Gelfand. 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 Alan E. Gelfand. The network helps show where Alan E. Gelfand may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 318 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Sampling-Based Approaches to Calculating Marginal Densities
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19904602
2
Hierarchical Modeling and Analysis for Spatial Data
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20031266
3
Hierarchical Modeling and Analysis for Spatial Data
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20141159
4
Sampling-Based Approaches to Calculating Marginal Densities
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19901022
5
Bayesian Model Choice: Asymptotics and Exact Calculations
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1994850
6
Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling
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1990656
7
Gaussian Predictive Process Models for Large Spatial Data Sets
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2008624
8
Bayesian Statistics without Tears: A Sampling–Resampling Perspective
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1992613
9
Handbook of Spatial Statistics
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2010571
10
Model choice: a minimum posterior predictive loss approach
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1998537
11 2003412
12 1992401
13 1997367
14
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets
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2015356
15 2003312
16 1992303
17 2006296
18 1992267
19 1994227
20 2004221

About Alan E. Gelfand

Alan E. Gelfand is a scholar working on Statistics and Probability, Artificial Intelligence, Economics and Econometrics, Environmental Engineering and Global and Planetary Change, having authored 318 papers that have together received 24.1k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (71 papers), Soil Geostatistics and Mapping (61 papers), Bayesian Methods and Mixture Models (60 papers), Spatial and Panel Data Analysis (56 papers), Statistical Methods and Inference (50 papers), Economic and Environmental Valuation (39 papers), Ecology and Vegetation Dynamics Studies (31 papers) and Species Distribution and Climate Change (22 papers). The work is most often cited by research in Statistics and Probability (8.3k citations), Ecological Modeling (1.1k citations), Environmental Engineering (3.7k citations), Economics and Econometrics (5.3k citations) and Artificial Intelligence (6.0k citations). Alan E. Gelfand has collaborated with scholars based in United States, United Kingdom and Spain. Frequent co-authors include A. F. M. Smith, Sudipto Banerjee, Bradley P. Carlin, Dipak K. Dey, Andrew O. Finley, Huiyan Sang, Sujit K. Sahu, David M. Holland, Amy Racine-Poon and Athanasios Kottas. Their work appears in journals such as Journal of the American Statistical Association, Journal of Agricultural Biological and Environmental Statistics, Spatial Statistics, Journal of the Royal Statistical Society Series C (Applied Statistics) and Environmetrics.

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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