David R. Bickel

1.4k total citations
90 papers, 934 citations indexed

About

David R. Bickel is a scholar working on Statistics and Probability, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, David R. Bickel has authored 90 papers receiving a total of 934 indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Statistics and Probability, 37 papers in Molecular Biology and 23 papers in Artificial Intelligence. Recurrent topics in David R. Bickel's work include Statistical Methods in Clinical Trials (26 papers), Statistical Methods and Bayesian Inference (25 papers) and Gene expression and cancer classification (21 papers). David R. Bickel is often cited by papers focused on Statistical Methods in Clinical Trials (26 papers), Statistical Methods and Bayesian Inference (25 papers) and Gene expression and cancer classification (21 papers). David R. Bickel collaborates with scholars based in Canada, United States and Taiwan. David R. Bickel's co-authors include R. Frühwirth, Bruce J. West, O. S. Smith, Mary A. Rupe, Mei Guo, Bin Hu, Véronic Bézaire, Sean H. Adams, Mary‐Ellen Harper and Oliver Fiehn and has published in prestigious journals such as Bioinformatics, PLoS ONE and The FASEB Journal.

In The Last Decade

David R. Bickel

84 papers receiving 900 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David R. Bickel Canada 15 363 309 187 132 92 90 934
Daniel Barry Ireland 10 301 0.8× 241 0.8× 271 1.4× 153 1.2× 54 0.6× 28 1.0k
Małgorzata Bogdan Poland 17 297 0.8× 119 0.4× 126 0.7× 269 2.0× 44 0.5× 67 1.2k
Xianyang Zhang United States 21 325 0.9× 362 1.2× 114 0.6× 62 0.5× 31 0.3× 70 1.2k
Julia Mortera Italy 18 177 0.5× 267 0.9× 317 1.7× 475 3.6× 46 0.5× 42 927
Ola Hössjer Sweden 22 666 1.8× 127 0.4× 175 0.9× 363 2.8× 250 2.7× 118 1.6k
Sarah Filippi United Kingdom 16 157 0.4× 370 1.2× 236 1.3× 78 0.6× 44 0.5× 36 929
Reza Modarres United States 16 418 1.2× 76 0.2× 197 1.1× 34 0.3× 130 1.4× 81 940
Markus Kalisch Switzerland 14 340 0.9× 384 1.2× 432 2.3× 72 0.5× 24 0.3× 29 1.2k
Arnold Janssen Germany 12 426 1.2× 322 1.0× 131 0.7× 64 0.5× 27 0.3× 51 901
Eric Slud United States 19 585 1.6× 109 0.4× 164 0.9× 22 0.2× 81 0.9× 84 1.2k

Countries citing papers authored by David R. Bickel

Since Specialization
Citations

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

Fields of papers citing papers by David R. Bickel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David R. Bickel. 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 David R. Bickel. The network helps show where David R. Bickel may publish in the future.

Co-authorship network of co-authors of David R. Bickel

This figure shows the co-authorship network connecting the top 25 collaborators of David R. Bickel. A scholar is included among the top collaborators of David R. Bickel 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 David R. Bickel. David R. Bickel 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.
2.
Bickel, David R.. (2023). Statistical evidence and surprise unified under possibility theory. Scandinavian Journal of Statistics. 50(3). 923–928. 1 indexed citations
3.
Bickel, David R.. (2023). Errata: Interval estimation, point estimation, and null hypothesis significance testing calibrated by an estimated posterior probability of the null hypothesis Bickel (2023). Communication in Statistics- Theory and Methods. 53(14). 5297–5297. 1 indexed citations
4.
5.
Bickel, David R.. (2021). The sufficiency of the evidence, the relevancy of the evidence, and quantifying both with a single number. Statistical Methods & Applications. 3 indexed citations
6.
Bickel, David R.. (2021). Model averages sharpened into Occam’s razors: Deep learning enhanced by Rényi entropy. Communication in Statistics- Theory and Methods. 51(23). 8283–8295.
7.
Bickel, David R.. (2020). Null Hypothesis Significance Testing Interpreted and Calibrated by Estimating Probabilities of Sign Errors: A Bayes-Frequentist Continuum. The American Statistician. 75(1). 104–112. 7 indexed citations
8.
Bickel, David R.. (2019). An Explanatory Rationale for Priors Sharpened Into Occam’s Razors. Bayesian Analysis. 15(4). 4 indexed citations
9.
Bickel, David R., et al.. (2018). Incorporating Prior Knowledge about Genetic Variants into the Analysis of Genetic Association Data: An Empirical Bayes Approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 17(2). 635–646. 5 indexed citations
10.
Bickel, David R.. (2015). Inference after checking multiple Bayesian models for data conflict and applications to mitigating the influence of rejected priors. International Journal of Approximate Reasoning. 66. 53–72. 13 indexed citations
11.
Bickel, David R.. (2012). Empirical Bayes Interval Estimates that are Conditionally Equal to Unadjusted Confidence Intervals or to Default Prior Credibility Intervals. Statistical Applications in Genetics and Molecular Biology. 11(3). Article 7–Article 7. 11 indexed citations
12.
Montazeri, Zahra, et al.. (2010). Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression. Statistical Applications in Genetics and Molecular Biology. 9(1). Article23–Article23. 10 indexed citations
13.
Bickel, David R.. (2008). Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study. Statistical Applications in Genetics and Molecular Biology. 7(1). Article10–Article10. 4 indexed citations
14.
Bickel, David R.. (2006). Incorporating expert knowledge into frequentist results by combining subjective prior and objective posterior distributions: A generalization of confidence distribution combination. arXiv (Cornell University). 1 indexed citations
15.
Bickel, David R.. (2004). Degrees of Differential Gene Expression. SSRN Electronic Journal. 1 indexed citations
16.
Bickel, David R.. (2003). Robust and efficient estimation of the mode of continuous data: the mode as a viable measure of central tendency. Journal of Statistical Computation and Simulation. 73(12). 899–912. 41 indexed citations
17.
Bickel, David R.. (2002). Selecting an Optimal Rejection Region for Multiple Testing: A decision theory alternative to FDR control, with an application to microarrays. arXiv (Cornell University). 4 indexed citations
18.
Bickel, David R.. (2002). Error-rate and decision-theoretic methods of multiple testing: Alternatives to controling conventional false discovery rates, with an application to microarrays. arXiv (Cornell University). 1 indexed citations
19.
Bickel, David R.. (2002). Generalized entropy and multifractality of time-series: relationship between order and intermittency. Chaos Solitons & Fractals. 13(3). 491–497. 9 indexed citations
20.
Bickel, David R. & Bruce J. West. (1998). Molecular Evolution Modeled as a Fractal Renewal Point Process in Agreement with the Dispersion of Substitutions in Mammalian Genes. Journal of Molecular Evolution. 47(5). 551–556. 11 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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