Antonio R. Linero

863 total citations
28 papers, 465 citations indexed

About

Antonio R. Linero is a scholar working on Statistics and Probability, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Antonio R. Linero has authored 28 papers receiving a total of 465 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics and Probability, 17 papers in Artificial Intelligence and 2 papers in Statistics, Probability and Uncertainty. Recurrent topics in Antonio R. Linero's work include Statistical Methods and Inference (20 papers), Statistical Methods and Bayesian Inference (14 papers) and Bayesian Methods and Mixture Models (11 papers). Antonio R. Linero is often cited by papers focused on Statistical Methods and Inference (20 papers), Statistical Methods and Bayesian Inference (14 papers) and Bayesian Methods and Mixture Models (11 papers). Antonio R. Linero collaborates with scholars based in United States, United Kingdom and Cyprus. Antonio R. Linero's co-authors include Jared S. Murray, Jennifer Hill, Yun Yang, Michael J. Daniels, Eric Chicken, J. Piekarewicz, Debajyoti Sinha, Joseph Antonelli, Qian Zhang and Junliang Du and has published in prestigious journals such as Journal of the American Statistical Association, Biometrics and Biometrika.

In The Last Decade

Antonio R. Linero

24 papers receiving 454 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Antonio R. Linero United States 10 236 148 46 40 34 28 465
Olivier Lopez France 14 131 0.6× 96 0.6× 64 1.4× 7 0.2× 86 2.5× 47 567
David I. Hastie United Kingdom 9 108 0.5× 136 0.9× 24 0.5× 4 0.1× 10 0.3× 10 382
Abdulkadir Hussein Canada 11 117 0.5× 17 0.1× 28 0.6× 30 0.8× 24 0.7× 47 460
S. Picoli Brazil 14 86 0.4× 33 0.2× 160 3.5× 6 0.1× 12 0.4× 25 504
Satya N. Mishra United States 10 162 0.7× 64 0.4× 42 0.9× 2 0.1× 45 1.3× 24 491
KK 12 201 0.9× 131 0.9× 186 4.0× 3 0.1× 89 2.6× 18 797
Genya Kobayashi Japan 7 252 1.1× 166 1.1× 80 1.7× 32 0.9× 17 431
Pavel Čı́žek Netherlands 11 183 0.8× 39 0.3× 133 2.9× 10 0.3× 65 1.9× 50 484
Samuel Livingstone United Kingdom 5 122 0.5× 102 0.7× 18 0.4× 10 0.3× 12 0.4× 9 309
Samuel Taylor United States 6 53 0.2× 19 0.1× 25 0.5× 5 0.1× 14 0.4× 10 431

Countries citing papers authored by Antonio R. Linero

Since Specialization
Citations

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

Fields of papers citing papers by Antonio R. Linero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Antonio R. Linero

This figure shows the co-authorship network connecting the top 25 collaborators of Antonio R. Linero. A scholar is included among the top collaborators of Antonio R. Linero 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 Antonio R. Linero. Antonio R. Linero 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.
Doss, Hani & Antonio R. Linero. (2024). Scalable Empirical Bayes Inference and Bayesian Sensitivity Analysis. Statistical Science. 39(4). 601–622.
2.
Linero, Antonio R.. (2024). Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy. Journal of the American Statistical Association. 120(549). 356–369. 3 indexed citations
3.
Linero, Antonio R.. (2023). Prior and Posterior Checking of Implicit Causal Assumptions. Biometrics. 79(4). 3153–3164.
4.
Linero, Antonio R.. (2023). In Nonparametric and High-Dimensional Models, Bayesian Ignorability is an Informative Prior. Journal of the American Statistical Association. 119(548). 2785–2798.
5.
Linero, Antonio R. & Qian Zhang. (2022). Mediation analysis using Bayesian tree ensembles.. Psychological Methods. 30(1). 60–82. 7 indexed citations
6.
Linero, Antonio R. & Junliang Du. (2022). Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners. Journal of Computational and Graphical Statistics. 32(3). 1046–1059. 1 indexed citations
7.
Linero, Antonio R., et al.. (2022). Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles. Journal of the American Statistical Association. 118(543). 2129–2142. 7 indexed citations
8.
Linero, Antonio R. & Joseph Antonelli. (2022). The how and why of Bayesian nonparametric causal inference. Wiley Interdisciplinary Reviews Computational Statistics. 15(1). 9 indexed citations
9.
Linero, Antonio R., et al.. (2022). Bayesian additive regression trees for multivariate skewed responses. Statistics in Medicine. 42(3). 246–263. 5 indexed citations
10.
Linero, Antonio R., et al.. (2021). Bayesian Survival Tree Ensembles with Submodel Shrinkage. Bayesian Analysis. 17(3). 13 indexed citations
11.
Linero, Antonio R.. (2021). Simulation‐based estimators of analytically intractable causal effects. Biometrics. 78(3). 1001–1017. 4 indexed citations
12.
Chicken, Eric, et al.. (2020). Computationally efficient Bayesian sequential function monitoring. Journal of Quality Technology. 54(1). 1–19. 2 indexed citations
13.
Du, Junliang & Antonio R. Linero. (2019). Incorporating Grouping Information into Bayesian Decision Tree Ensembles. International Conference on Machine Learning. 1686–1695. 2 indexed citations
14.
Hill, Jennifer, Antonio R. Linero, & Jared S. Murray. (2019). Bayesian Additive Regression Trees: A Review and Look Forward. Annual Review of Statistics and Its Application. 7(1). 251–278. 102 indexed citations
15.
Linero, Antonio R. & Michael J. Daniels. (2018). Bayesian Approaches for Missing Not at Random Outcome Data: The Role of Identifying Restrictions. Statistical Science. 33(2). 198–213. 31 indexed citations
16.
Linero, Antonio R., Jonathan R. Bradley, & Apurva A. Desai. (2018). Multi-rubric models for ordinal spatial data with application to online ratings data. The Annals of Applied Statistics. 12(4). 5 indexed citations
17.
Du, Junliang & Antonio R. Linero. (2018). Interaction Detection with Bayesian Decision Tree Ensembles. arXiv (Cornell University). 108–117. 5 indexed citations
18.
Linero, Antonio R. & Yun Yang. (2018). Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity. Journal of the Royal Statistical Society Series B (Statistical Methodology). 80(5). 1087–1110. 58 indexed citations
19.
Piekarewicz, J., et al.. (2016). Power of two: Assessing the impact of a second measurement of the weak-charge form factor ofPb208. Physical review. C. 94(3). 41 indexed citations
20.
Linero, Antonio R. & Michael J. Daniels. (2014). A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial. Journal of the American Statistical Association. 110(509). 45–55. 22 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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