Luis M. Castro

759 total citations
44 papers, 415 citations indexed

About

Luis M. Castro is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Luis M. Castro has authored 44 papers receiving a total of 415 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Statistics and Probability, 22 papers in Artificial Intelligence and 3 papers in Management Science and Operations Research. Recurrent topics in Luis M. Castro's work include Statistical Methods and Bayesian Inference (27 papers), Bayesian Methods and Mixture Models (22 papers) and Statistical Distribution Estimation and Applications (22 papers). Luis M. Castro is often cited by papers focused on Statistical Methods and Bayesian Inference (27 papers), Bayesian Methods and Mixture Models (22 papers) and Statistical Distribution Estimation and Applications (22 papers). Luis M. Castro collaborates with scholars based in Chile, Brazil and United States. Luis M. Castro's co-authors include Víctor H. Lachos, Reinaldo B. Arellano‐Valle, Héctor W. Gómez, Wan‐Lun Wang, Dipak K. Dey, Tsung‐I Lin, Dipankar Bandyopadhyay, Ernesto San Martı́n, Marc G. Genton and Graciela González–Farías and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Biometrics.

In The Last Decade

Luis M. Castro

41 papers receiving 407 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Luis M. Castro Chile	12	334	188	45	43	38	44	415
Ursula U. Müller United States	11	237 0.7×	77 0.4×	22 0.5×	31 0.7×	33 0.9×	31	311
Hong-Tu Zhu Hong Kong	7	300 0.9×	137 0.7×	56 1.2×	24 0.6×	12 0.3×	9	374
Luis E. Nieto‐Barajas Mexico	11	186 0.6×	161 0.9×	30 0.7×	13 0.3×	32 0.8×	35	296
Alessandro Barbiero Italy	10	183 0.5×	63 0.3×	42 0.9×	72 1.7×	18 0.5×	46	267
Ching‐Kang Ing Taiwan	9	135 0.4×	40 0.2×	67 1.5×	25 0.6×	60 1.6×	26	260
Larry M. Pearson United States	7	188 0.6×	40 0.2×	41 0.9×	40 0.9×	14 0.4×	14	288
S.K. Upadhyay India	13	338 1.0×	52 0.3×	43 1.0×	204 4.7×	17 0.4×	52	437
Luisa Turrin Fernholz United States	10	230 0.7×	57 0.3×	45 1.0×	64 1.5×	39 1.0×	18	312
A. W. Bowman United Kingdom	5	206 0.6×	44 0.2×	30 0.7×	32 0.7×	29 0.8×	7	302
Alexander R. de Leon Canada	12	152 0.5×	123 0.7×	34 0.8×	10 0.2×	12 0.3×	29	419

Countries citing papers authored by Luis M. Castro

Since Specialization

Citations

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

Fields of papers citing papers by Luis M. Castro

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luis M. Castro. 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 Luis M. Castro. The network helps show where Luis M. Castro may publish in the future.

Co-authorship network of co-authors of Luis M. Castro

This figure shows the co-authorship network connecting the top 25 collaborators of Luis M. Castro. A scholar is included among the top collaborators of Luis M. Castro 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 Luis M. Castro. Luis M. Castro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Wan‐Lun, et al.. (2025). Mixtures of common factor analyzers using the restricted multivariate skew-t distribution for clustering high-dimensional data with missing values. Journal of Computational and Applied Mathematics. 471. 116708–116708.

Castro, Luis M., et al.. (2024). Identifying outbreaks in sewer networks: An adaptive sampling scheme under network’s uncertainty. Proceedings of the National Academy of Sciences. 121(14). e2316616121–e2316616121. 1 indexed citations

Abanto‐Valle, Carlos A., et al.. (2023). A Bayesian approach for mixed effects state‐space models under skewness and heavy tails. Biometrical Journal. 65(8). e2100302–e2100302.

Bevilacqua, Moreno, et al.. (2022). Modeling Point Referenced Spatial Count Data: A Poisson Process Approach. Journal of the American Statistical Association. 119(545). 664–677. 7 indexed citations

Lachos, Víctor H., et al.. (2022). Extending multivariate Student's‐t$$ t $$ semiparametric mixed models for longitudinal data with censored responses and heavy tails. Statistics in Medicine. 41(19). 3696–3719. 4 indexed citations

Page, Garritt L., et al.. (2022). Joint Random Partition Models for Multivariate Change Point Analysis. Bayesian Analysis. 19(1). 5 indexed citations

Castro, Luis M., et al.. (2021). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis. 189. 104944–104944. 9 indexed citations

Brownstein, Naomi C., Veronica Bunn, Luis M. Castro, & Debajyoti Sinha. (2020). Bayesian analysis of survival data with missing censoring indicators. Biometrics. 77(1). 305–315. 2 indexed citations

Yang, Yuchen, Tsung‐I Lin, Luis M. Castro, & Wan‐Lun Wang. (2020). Extending finite mixtures of t linear mixed-effects models with concomitant covariates. Computational Statistics & Data Analysis. 148. 106961–106961. 5 indexed citations

10.

Wang, Wan‐Lun, Luis M. Castro, Víctor H. Lachos, & Tsung‐I Lin. (2019). Model-based clustering of censored data via mixtures of factor analyzers. Computational Statistics & Data Analysis. 140. 104–121. 18 indexed citations

11.

Lachos, Víctor H., et al.. (2018). Heavy-tailed longitudinal regression models for censored data: a robust parametric approach. Test. 28(3). 844–878. 8 indexed citations

12.

Wang, Wan‐Lun, et al.. (2018). Mixtures of restricted skew-t factor analyzers with common factor loadings. Advances in Data Analysis and Classification. 13(2). 445–480. 5 indexed citations

13.

Castro, Luis M., et al.. (2018). Quantile regression for nonlinear mixed effects models: a likelihood based perspective. Statistical Papers. 61(3). 1281–1307. 11 indexed citations

14.

Castro, Luis M., et al.. (2018). Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness. Statistical Methods in Medical Research. 28(5). 1457–1476. 7 indexed citations

15.

Lachos, Víctor H., et al.. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics. 60(2). 209–229. 2 indexed citations

16.

Wang, Wan‐Lun, Luis M. Castro, & Tsung‐I Lin. (2017). Automated learning oftfactor analysis models with complete and incomplete data. Journal of Multivariate Analysis. 161. 157–171. 7 indexed citations

17.

Bandyopadhyay, Dipankar, et al.. (2015). Influence assessment in censored mixed-effects models using the multivariate Student’s-tdistribution. Journal of Multivariate Analysis. 141. 104–117. 8 indexed citations

18.

Castro, Luis M., et al.. (2013). Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach. Statistical Methodology. 18. 14–31. 6 indexed citations

19.

Bandyopadhyay, Dipankar, Víctor H. Lachos, Luis M. Castro, & Dipak K. Dey. (2012). Skew‐normal/independent linear mixed models for censored responses with applications to HIV viral loads. Biometrical Journal. 54(3). 405–425. 29 indexed citations

20.

Valero, Paola, et al.. (1998). Calidad de la educación matemática en secundaria. Actores y procesos en la institución educativa. instname:Universidad de los Andes. 4 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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