Ruben H. Zamar

2.8k total citations
51 papers, 1.6k citations indexed

About

Ruben H. Zamar is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Control and Systems Engineering. According to data from OpenAlex, Ruben H. Zamar has authored 51 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Statistics and Probability, 31 papers in Statistics, Probability and Uncertainty and 5 papers in Control and Systems Engineering. Recurrent topics in Ruben H. Zamar's work include Advanced Statistical Methods and Models (49 papers), Statistical Methods and Inference (36 papers) and Advanced Statistical Process Monitoring (31 papers). Ruben H. Zamar is often cited by papers focused on Advanced Statistical Methods and Models (49 papers), Statistical Methods and Inference (36 papers) and Advanced Statistical Process Monitoring (31 papers). Ruben H. Zamar collaborates with scholars based in Canada, Argentina and Belgium. Ruben H. Zamar's co-authors include Vı́ctor J. Yohai, Ricardo A. Maronna, Stefan Van Aelst, R. Douglas Martin, Matías Salibián‐Barrera, Fatemah Alqallaf, V. J. Yohai, Gert Willems, Claudio Agostinelli and Xiaogang Wang and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and Biometrics.

In The Last Decade

Ruben H. Zamar

49 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruben H. Zamar Canada 19 1.2k 659 207 123 103 51 1.6k
Graciela Boente Argentina 18 964 0.8× 308 0.5× 218 1.1× 76 0.6× 104 1.0× 86 1.2k
Hendrik P. Lopuhaä Netherlands 16 761 0.7× 400 0.6× 185 0.9× 69 0.6× 60 0.6× 36 1.3k
A. K. Md. Ehsanes Saleh Canada 22 1.3k 1.2× 483 0.7× 227 1.1× 87 0.7× 54 0.5× 117 1.8k
Regina Y. Liu United States 23 1.6k 1.4× 898 1.4× 293 1.4× 145 1.2× 63 0.6× 44 2.3k
Marco Riani Italy 21 952 0.8× 488 0.7× 325 1.6× 68 0.6× 92 0.9× 88 1.7k
Robert G. Staudte Australia 17 865 0.8× 350 0.5× 114 0.6× 60 0.5× 52 0.5× 54 1.4k
P. L. Davies Germany 12 655 0.6× 332 0.5× 113 0.5× 61 0.5× 84 0.8× 34 944
Jana Jurečková Czechia 20 1.3k 1.1× 305 0.5× 157 0.8× 115 0.9× 47 0.5× 85 1.6k
Ibrahim A. Ahmad United States 21 1.1k 0.9× 322 0.5× 277 1.3× 132 1.1× 32 0.3× 101 1.5k
Jan Hannig United States 21 939 0.8× 322 0.5× 327 1.6× 45 0.4× 79 0.8× 90 1.6k

Countries citing papers authored by Ruben H. Zamar

Since Specialization
Citations

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

Fields of papers citing papers by Ruben H. Zamar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruben H. Zamar. 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 Ruben H. Zamar. The network helps show where Ruben H. Zamar may publish in the future.

Co-authorship network of co-authors of Ruben H. Zamar

This figure shows the co-authorship network connecting the top 25 collaborators of Ruben H. Zamar. A scholar is included among the top collaborators of Ruben H. Zamar 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 Ruben H. Zamar. Ruben H. Zamar 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.
Aelst, Stefan Van, et al.. (2024). Multi-model subset selection. Computational Statistics & Data Analysis. 203. 108073–108073.
2.
Zhang, Hongyang, et al.. (2016). Robust regression estimation and inference in the presence of cellwise and casewise contamination. Computational Statistics & Data Analysis. 99. 1–11. 14 indexed citations
3.
Freue, Gabriela V. Cohen, Hernán Ortiz‐Molina, & Ruben H. Zamar. (2013). A Natural Robustification of the Ordinary Instrumental Variables Estimator. Biometrics. 69(3). 641–650. 12 indexed citations
4.
Aelst, Stefan Van, Gert Willems, & Ruben H. Zamar. (2013). Robust and efficient estimation of the residual scale in linear regression. Journal of Multivariate Analysis. 116. 278–296. 12 indexed citations
5.
Yohai, Vı́ctor J., et al.. (2012). Robust Estimation of Multivariate Location and Scatter in the Presence of Missing Data. Journal of the American Statistical Association. 107(499). 1178–1186. 21 indexed citations
6.
Aelst, Stefan Van, et al.. (2010). Fast robust estimation of prediction error based on resampling. Computational Statistics & Data Analysis. 54(12). 3121–3130. 22 indexed citations
7.
Aelst, Stefan Van, Roy E. Welsch, & Ruben H. Zamar. (2010). Special issue on variable selection and robust procedures. Computational Statistics & Data Analysis. 54(12). 2879–2882. 3 indexed citations
8.
García-Escudero, Luis Ángel, Alfonso Gordaliza, R. San Martín, Stefan Van Aelst, & Ruben H. Zamar. (2008). Robust Linear Clustering. Journal of the Royal Statistical Society Series B (Statistical Methodology). 71(1). 301–318. 31 indexed citations
9.
García-Escudero, Luis Ángel, et al.. (2007). Robust linear clustering around affine subspaces. 285. 1 indexed citations
10.
Aelst, Stefan Van, et al.. (2007). Robust Linear Model Selection Based on Least Angle Regression. Journal of the American Statistical Association. 102(480). 1289–1299. 113 indexed citations
11.
Berrendero, José R. & Ruben H. Zamar. (2006). A note on the uniform asymptotic normality of location M-estimates. Metrika. 63(1). 55–69. 3 indexed citations
12.
Aelst, Stefan Van, Xiaogang Wang, Ruben H. Zamar, & Rong Zhu. (2004). Linear grouping using orthogonal regression. Computational Statistics & Data Analysis. 50(5). 1287–1312. 47 indexed citations
13.
Alqallaf, Fatemah, et al.. (2002). Scalable robust covariance and correlation estimates for data mining. 6 indexed citations
14.
Fraiman, Ricardo, Vı́ctor J. Yohai, & Ruben H. Zamar. (2001). Optimal Robust M-Estimates of Location. The Annals of Statistics. 29(1). 22 indexed citations
15.
Berrendero, José R. & Ruben H. Zamar. (2001). Maximum Bias Curves for Robust Regression with Non-elliptical Regressors. The Annals of Statistics. 29(1). 12 indexed citations
16.
Kelmansky, Diana, et al.. (1999). A Class of Locally and Globally Robust Regression Estimates. Journal of the American Statistical Association. 94(445). 174–188. 13 indexed citations
17.
Berrendero, José R., et al.. (1998). On the explosion rate of maximum‐bias functions. Canadian Journal of Statistics. 26(2). 333–351. 6 indexed citations
18.
Li, Bing & Ruben H. Zamar. (1996). M‐Estimates of regression when the scale is unknown and the error distribution is possibly asymmetric: A minimax result. Canadian Journal of Statistics. 24(2). 193–206. 2 indexed citations
19.
Martin, R. Douglas & Ruben H. Zamar. (1993). Efficiency-Constrained Bias-Robust Estimation of Location. The Annals of Statistics. 21(1). 16 indexed citations
20.
Yohai, Vı́ctor J. & Ruben H. Zamar. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association. 83(402). 406–413. 223 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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