Aldo M. Garay

462 total citations
19 papers, 304 citations indexed

About

Aldo M. Garay is a scholar working on Statistics and Probability, Artificial Intelligence and Finance. According to data from OpenAlex, Aldo M. Garay has authored 19 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Statistics and Probability, 13 papers in Artificial Intelligence and 3 papers in Finance. Recurrent topics in Aldo M. Garay's work include Statistical Distribution Estimation and Applications (14 papers), Bayesian Methods and Mixture Models (13 papers) and Statistical Methods and Bayesian Inference (12 papers). Aldo M. Garay is often cited by papers focused on Statistical Distribution Estimation and Applications (14 papers), Bayesian Methods and Mixture Models (13 papers) and Statistical Methods and Bayesian Inference (12 papers). Aldo M. Garay collaborates with scholars based in Brazil, United States and Taiwan. Aldo M. Garay's co-authors include Víctor H. Lachos, Edwin M. M. Ortega, Elizabeth M. Hashimoto, Celso Rômulo Barbosa Cabral, Heleno Bolfarine, Carlos A. Abanto‐Valle, Dipankar Bandyopadhyay, Tsung‐I Lin, Jacek Leśkow and Luis M. Castro and has published in prestigious journals such as Computational Statistics & Data Analysis, Statistical Methods in Medical Research and Journal of Statistical Planning and Inference.

In The Last Decade

Aldo M. Garay

16 papers receiving 300 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aldo M. Garay Brazil 11 230 130 27 26 26 19 304
Tereza Neocleous United Kingdom 9 103 0.4× 66 0.5× 11 0.4× 5 0.2× 25 1.0× 18 274
Qiqing Yu United States 11 399 1.7× 119 0.9× 59 2.2× 19 0.7× 28 1.1× 73 486
Luis E. Nieto‐Barajas Mexico 11 186 0.8× 161 1.2× 13 0.5× 32 1.2× 38 1.5× 35 296
Luis M. Castro Chile 12 334 1.5× 188 1.4× 43 1.6× 38 1.5× 16 0.6× 44 415
Vandna Jowaheer Mauritius 12 174 0.8× 40 0.3× 15 0.6× 111 4.3× 73 2.8× 48 373
Angelo Mazza Italy 10 91 0.4× 92 0.7× 11 0.4× 25 1.0× 52 2.0× 26 281
Feng‐Chang Xie China 11 274 1.2× 83 0.6× 55 2.0× 15 0.6× 16 0.6× 20 309
Xingwei Tong China 17 604 2.6× 129 1.0× 35 1.3× 11 0.4× 141 5.4× 68 760
Adriano K. Suzuki Brazil 10 232 1.0× 65 0.5× 86 3.2× 30 1.2× 14 0.5× 55 294
Minggao Gu United States 9 301 1.3× 61 0.5× 19 0.7× 13 0.5× 31 1.2× 14 338

Countries citing papers authored by Aldo M. Garay

Since Specialization
Citations

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

Fields of papers citing papers by Aldo M. Garay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aldo M. Garay

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

All Works

19 of 19 papers shown
1.
2.
Garay, Aldo M., et al.. (2024). Bayesian analysis of linear regression models with autoregressive symmetrical errors and incomplete data. Statistical Papers. 65(9). 5649–5690.
3.
Garay, Aldo M., et al.. (2020). Mixed-effects models for censored data with autoregressive errors. Journal of Biopharmaceutical Statistics. 31(3). 273–294.
4.
Garay, Aldo M., et al.. (2020). Bayesian analysis of the p-order integer-valued AR process with zero-inflated Poisson innovations. Journal of Statistical Computation and Simulation. 90(11). 1943–1964. 2 indexed citations
5.
Lachos, Víctor H., Aldo M. Garay, & Celso Rômulo Barbosa Cabral. (2020). Moments of truncated scale mixtures of skew-normal distributions. Brazilian Journal of Probability and Statistics. 34(3). 6 indexed citations
6.
Lachos, Víctor H., et al.. (2019). Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions. Journal of Applied Statistics. 47(9). 1690–1719. 6 indexed citations
7.
Garay, Aldo M., et al.. (2017). Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions. Journal of Applied Statistics. 45(11). 2039–2066. 18 indexed citations
8.
Lachos, Víctor H., et al.. (2017). Influence diagnostics in spatial models with censored response. Environmetrics. 28(7). 6 indexed citations
9.
Garay, Aldo M., et al.. (2017). Bayesian analysis of censored linear regression models with scale mixtures of skew-normal distributions. Statistics and Its Interface. 10(3). 425–439. 11 indexed citations
10.
Garay, Aldo M., Víctor H. Lachos, & Tsung‐I Lin. (2016). Nonlinear censored regression models with heavy-tailed distributions. Statistics and Its Interface. 9(3). 281–293. 9 indexed citations
11.
Garay, Aldo M., Víctor H. Lachos, & Heleno Bolfarine. (2015). Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model. Journal of Applied Statistics. 42(6). 1148–1165. 11 indexed citations
12.
Garay, Aldo M., Heleno Bolfarine, Víctor H. Lachos, & Celso Rômulo Barbosa Cabral. (2015). Bayesian analysis of censored linear regression models with scale mixtures of normal distributions. Journal of Applied Statistics. 42(12). 2694–2714. 21 indexed citations
13.
Garay, Aldo M., Víctor H. Lachos, Heleno Bolfarine, & Celso Rômulo Barbosa Cabral. (2015). Linear censored regression models with scale mixtures of normal distributions. Statistical Papers. 58(1). 247–278. 34 indexed citations
14.
Garay, Aldo M., Luis M. Castro, Jacek Leśkow, & Víctor H. Lachos. (2014). Censored linear regression models for irregularly observed longitudinal data using the multivariate-t distribution. Statistical Methods in Medical Research. 26(2). 542–566. 14 indexed citations
15.
Garay, Aldo M., et al.. (2013). Statistical diagnostics for nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of Statistical Computation and Simulation. 84(8). 1761–1778. 12 indexed citations
16.
Garay, Aldo M., et al.. (2012). Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of Statistical Planning and Inference. 142(7). 2149–2165. 24 indexed citations
17.
Lachos, Víctor H., Dipankar Bandyopadhyay, & Aldo M. Garay. (2011). Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions. Statistics & Probability Letters. 81(8). 1208–1217. 14 indexed citations
18.
Garay, Aldo M., Víctor H. Lachos, & Carlos A. Abanto‐Valle. (2010). Nonlinear regression models based on scale mixtures of skew-normal distributions. Journal of the Korean Statistical Society. 40(1). 115–124. 20 indexed citations
19.
Garay, Aldo M., Elizabeth M. Hashimoto, Edwin M. M. Ortega, & Víctor H. Lachos. (2010). On estimation and influence diagnostics for zero-inflated negative binomial regression models. Computational Statistics & Data Analysis. 55(3). 1304–1318. 96 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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