Fred W. Huffer

732 total citations
32 papers, 461 citations indexed

About

Fred W. Huffer is a scholar working on Statistics and Probability, Artificial Intelligence and Mathematical Physics. According to data from OpenAlex, Fred W. Huffer has authored 32 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Statistics and Probability, 8 papers in Artificial Intelligence and 6 papers in Mathematical Physics. Recurrent topics in Fred W. Huffer's work include Statistical Methods and Inference (9 papers), Bayesian Methods and Mixture Models (7 papers) and Stochastic processes and statistical mechanics (6 papers). Fred W. Huffer is often cited by papers focused on Statistical Methods and Inference (9 papers), Bayesian Methods and Mixture Models (7 papers) and Stochastic processes and statistical mechanics (6 papers). Fred W. Huffer collaborates with scholars based in United States, South Korea and Taiwan. Fred W. Huffer's co-authors include Ian W. McKeague, Hulin Wu, Cheolyong Park, Chien‐Tai Lin, L. A. Shepp, Persi Diaconis, Michael D. Perlman, Mary Ellen Bock, Anthony J. Arnold and William C. Parker and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Biometrics.

In The Last Decade

Fred W. Huffer

31 papers receiving 426 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fred W. Huffer United States 10 258 73 68 57 37 32 461
Kunio Shimizu Japan 13 183 0.7× 166 2.3× 36 0.5× 26 0.5× 62 1.7× 62 607
Nicholas A. James United States 4 131 0.5× 88 1.2× 25 0.4× 50 0.9× 28 0.8× 7 477
Henri Caussinus France 10 140 0.5× 79 1.1× 37 0.5× 54 0.9× 11 0.3× 25 628
А. В. Иванов Ukraine 12 144 0.6× 59 0.8× 48 0.7× 42 0.7× 166 4.5× 67 545
Murray Jorgensen New Zealand 11 115 0.4× 202 2.8× 20 0.3× 123 2.2× 23 0.6× 26 697
Gopalan Nair Australia 15 113 0.4× 62 0.8× 24 0.4× 86 1.5× 26 0.7× 38 724
Peter de Jong Netherlands 5 209 0.8× 58 0.8× 36 0.5× 140 2.5× 55 1.5× 5 458
K. R. W. Brewer Australia 14 392 1.5× 156 2.1× 119 1.8× 134 2.4× 57 1.5× 32 757
Hirokazu Yanagihara Japan 17 500 1.9× 149 2.0× 91 1.3× 48 0.8× 15 0.4× 91 804
Jesse Windle United States 4 259 1.0× 253 3.5× 34 0.5× 55 1.0× 24 0.6× 6 566

Countries citing papers authored by Fred W. Huffer

Since Specialization
Citations

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

Fields of papers citing papers by Fred W. Huffer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fred W. Huffer. 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 Fred W. Huffer. The network helps show where Fred W. Huffer may publish in the future.

Co-authorship network of co-authors of Fred W. Huffer

This figure shows the co-authorship network connecting the top 25 collaborators of Fred W. Huffer. A scholar is included among the top collaborators of Fred W. Huffer 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 Fred W. Huffer. Fred W. Huffer 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.
Huffer, Fred W. & Cheolyong Park. (2020). A Simple Rule for Monitoring the Error Rate of Random Forest for Classification. 39(1). 1–15. 2 indexed citations
2.
Huffer, Fred W., et al.. (2014). A general class of test statistics for Van Valen's Red Queen hypothesis. Journal of Applied Statistics. 41(9). 2028–2043. 2 indexed citations
3.
Su, Jingyong, Anuj Srivastava, & Fred W. Huffer. (2012). Detection, classification and estimation of individual shapes in 2D and 3D point clouds. Computational Statistics & Data Analysis. 58. 227–241. 14 indexed citations
4.
Huffer, Fred W., et al.. (2011). Additive Risk Model Using Piecewise Constant Hazard Function. Communications in Statistics - Simulation and Computation. 40(9). 1458–1477. 1 indexed citations
5.
Huffer, Fred W., et al.. (2007). An explanatory differential item functioning (DIF) model by the WinBUG 1.4. SHILAP Revista de lepidopterología. 1 indexed citations
6.
Huffer, Fred W., et al.. (2006). A Bayesian Approach for Fitting a Random Effect Differential Item Functioning Across Group Units. 4. 27–41. 5 indexed citations
7.
Arnold, Anthony J., et al.. (2006). IS EXTINCTION AGE DEPENDENT?. Palaios. 21(6). 571–579. 28 indexed citations
8.
Huffer, Fred W. & Cheolyong Park. (2005). A test for elliptical symmetry. Journal of Multivariate Analysis. 98(2). 256–281. 42 indexed citations
9.
Arnold, Anthony J., et al.. (2004). Deviation from Red Queen behaviour at stratigraphic boundaries: evidence for directional recovery. Geological Society London Special Publications. 230(1). 35–46. 4 indexed citations
10.
Huffer, Fred W.. (2002). One-Dimensional Poisson Growth Models With Random and Asymmetric Growth. Methodology And Computing In Applied Probability. 4(3). 257–278. 2 indexed citations
11.
Huffer, Fred W. & Cheolyong Park. (2002). The limiting distribution of a test for multivariate structure. Journal of Statistical Planning and Inference. 105(2). 417–431. 2 indexed citations
12.
Huffer, Fred W. & Chien‐Tai Lin. (2001). COMPUTING THE JOINT DISTRIBUTION OF GENERAL LINEAR COMBINATIONS OF SPACINGS OR EXPONENTIAL VARIATES. 17 indexed citations
13.
Huffer, Fred W. & Cheolyong Park. (2000). A test for multivariate structure. Journal of Applied Statistics. 27(5). 633–650. 1 indexed citations
14.
Huffer, Fred W. & Hulin Wu. (1998). Markov Chain Monte Carlo for Autologistic Regression Models with Application to the Distribution of Plant Species. Biometrics. 54(2). 509–509. 93 indexed citations
15.
Doss, Hani, et al.. (1997). Bayesian nonparametric estimation via Gibbs sampling for coherent systems with redundancy. The Annals of Statistics. 25(3). 1 indexed citations
16.
Huffer, Fred W. & Chien‐Tai Lin. (1997). Approximating the Distribution of the Scan Statistic Using Moments of the Number of Clumps. Journal of the American Statistical Association. 92(440). 1466–1466. 5 indexed citations
17.
Huffer, Fred W. & Ian W. McKeague. (1991). Weighted least-squares estimation for Aalen's additive risk model. Technical report. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 16(11). 2241–4.
18.
Huffer, Fred W. & Ian W. McKeague. (1991). Weighted Least Squares Estimation for Aalen's Additive Risk Model. Journal of the American Statistical Association. 86(413). 114–114. 19 indexed citations
19.
Huffer, Fred W. & L. A. Shepp. (1987). On the probability of covering the circle by random arcs. Journal of Applied Probability. 24(2). 422–429. 9 indexed citations
20.
Bock, Mary Ellen, Persi Diaconis, Fred W. Huffer, & Michael D. Perlman. (1987). Inequalities for linear combinations of gamma random variables. Canadian Journal of Statistics. 15(4). 387–395. 34 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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