Pedro Galeano

1.4k total citations
40 papers, 843 citations indexed

About

Pedro Galeano is a scholar working on Statistics and Probability, Finance and Economics and Econometrics. According to data from OpenAlex, Pedro Galeano has authored 40 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics and Probability, 19 papers in Finance and 14 papers in Economics and Econometrics. Recurrent topics in Pedro Galeano's work include Financial Risk and Volatility Modeling (19 papers), Statistical Methods and Inference (15 papers) and Advanced Statistical Methods and Models (11 papers). Pedro Galeano is often cited by papers focused on Financial Risk and Volatility Modeling (19 papers), Statistical Methods and Inference (15 papers) and Advanced Statistical Methods and Models (11 papers). Pedro Galeano collaborates with scholars based in Spain, Germany and Colombia. Pedro Galeano's co-authors include Wenceslao González–Manteiga, Manuel Febrero–Bande, Daniel Peña, M. Concepción Ausín, Ruey S. Tsay, Dominik Wied, Rosa E. Lillo, Andrés M. Alonso, Nelson A. Canal and María del Rosario Castañeda and has published in prestigious journals such as Journal of the American Statistical Association, Technometrics and European Journal of Operational Research.

In The Last Decade

Pedro Galeano

39 papers receiving 819 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pedro Galeano Spain 15 354 240 220 218 134 40 843
Juan Romo Spain 16 596 1.7× 239 1.0× 188 0.9× 254 1.2× 240 1.8× 58 1.2k
Hélio S. Migon Brazil 15 517 1.5× 157 0.7× 180 0.8× 291 1.3× 116 0.9× 62 1.1k
Takeaki Kariya Japan 17 454 1.3× 152 0.6× 138 0.6× 113 0.5× 109 0.8× 68 892
Jens‐Peter Kreiß Germany 15 526 1.5× 469 2.0× 240 1.1× 128 0.6× 50 0.4× 46 969
Hisashi Tanizaki Japan 15 161 0.5× 182 0.8× 227 1.0× 367 1.7× 51 0.4× 41 895
Davy Paindaveine Belgium 17 716 2.0× 122 0.5× 79 0.4× 133 0.6× 176 1.3× 64 909
M. Pourahmadi United States 13 592 1.7× 119 0.5× 183 0.8× 240 1.1× 38 0.3× 23 944
Siegfried Hörmann United States 18 405 1.1× 494 2.1× 388 1.8× 98 0.4× 51 0.4× 42 1.1k
D. S. Poskitt Australia 22 375 1.1× 319 1.3× 354 1.6× 169 0.8× 90 0.7× 67 1.2k
Efstathios Paparoditis Cyprus 22 619 1.7× 551 2.3× 452 2.1× 194 0.9× 75 0.6× 71 1.4k

Countries citing papers authored by Pedro Galeano

Since Specialization
Citations

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

Fields of papers citing papers by Pedro Galeano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro Galeano. 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 Pedro Galeano. The network helps show where Pedro Galeano may publish in the future.

Co-authorship network of co-authors of Pedro Galeano

This figure shows the co-authorship network connecting the top 25 collaborators of Pedro Galeano. A scholar is included among the top collaborators of Pedro Galeano 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 Pedro Galeano. Pedro Galeano 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.
Ausín, M. Concepción, et al.. (2024). Structured factor copulas for modeling the systemic risk of European and United States banks. International Review of Financial Analysis. 96. 103621–103621. 1 indexed citations
2.
Febrero–Bande, Manuel, et al.. (2024). Testing for linearity in scalar-on-function regression with responses missing at random. Computational Statistics. 39(6). 3405–3429. 2 indexed citations
3.
Galeano, Pedro, et al.. (2020). Sequential detection of parameter changes in dynamic conditional correlation models. Applied Stochastic Models in Business and Industry. 37(3). 475–495. 2 indexed citations
4.
Ausín, M. Concepción, et al.. (2020). Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction. Energy Economics. 92. 104961–104961. 6 indexed citations
5.
Galeano, Pedro & Daniel Peña. (2019). Las nuevas oportunidades del Big Data para las instituciones financieras. Papeles de economía española. 78–97. 1 indexed citations
6.
Lopes, Hedibert F., et al.. (2019). Particle learning for Bayesian semi-parametric stochastic volatility model. Econometric Reviews. 38(9). 1007–1023. 10 indexed citations
7.
Wied, Dominik, et al.. (2016). Monitoring multivariate variance changes. Journal of Empirical Finance. 39. 54–68. 13 indexed citations
8.
Febrero–Bande, Manuel, Pedro Galeano, & Wenceslao González–Manteiga. (2015). Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study. International Statistical Review. 85(1). 61–83. 58 indexed citations
9.
Galeano, Pedro, et al.. (2014). Spatial depth-based classification for functional data. Test. 23(4). 725–750. 32 indexed citations
10.
Galeano, Pedro & Dominik Wied. (2013). Multiple break detection in the correlation structure of random variables. Computational Statistics & Data Analysis. 76. 262–282. 30 indexed citations
11.
Galeano, Pedro, et al.. (2013). Diversidad de parasitoides (hymenoptera) de moscas frugivoras (díptera tephritoidea) en dos áreas cafeteras del departamento del Tolima, Colombia. Dialnet (Universidad de la Rioja). 2(8). 4. 5 indexed citations
12.
Wied, Dominik & Pedro Galeano. (2012). Monitoring correlation change in a sequence of random variables. Journal of Statistical Planning and Inference. 143(1). 186–196. 20 indexed citations
13.
Galeano, Pedro & Ruey S. Tsay. (2010). Shifts in Individual Parameters of a GARCH Model. SSRN Electronic Journal. 2 indexed citations
14.
Castañeda, María del Rosario, et al.. (2010). Species, distribution and hosts of the genus Anastrepha Schiner in the Department of Tolima, Colombia. 28(2). 264–272. 6 indexed citations
15.
Castañeda, María del Rosario, Felipe Osorio, Nelson A. Canal, & Pedro Galeano. (2010). Especies, distribución y hospederos del género Anastrepha Schiner en el departamento del Tolima, Colombia. 28(2). 265–271. 14 indexed citations
16.
Canal, Nelson A., et al.. (2008). Daño de Myelobia sp. (Lepidoptera: Pyralidae) en plantaciones de guadua angustifolia Kunth en el departamento del Tolima. 1(3). 54–62. 1 indexed citations
17.
Febrero–Bande, Manuel, Pedro Galeano, & Wenceslao González–Manteiga. (2008). Measures of influence for the functional linear model with scalar response. Journal of Multivariate Analysis. 101(2). 327–339. 12 indexed citations
18.
Febrero–Bande, Manuel, Pedro Galeano, & Wenceslao González–Manteiga. (2007). A functional analysis of NOx levels: location and scale estimation and outlier detection. Computational Statistics. 22(3). 411–427. 52 indexed citations
19.
Ausín, M. Concepción & Pedro Galeano. (2006). Bayesian estimation of the Gaussian mixture GARCH model. Computational Statistics & Data Analysis. 51(5). 2636–2652. 52 indexed citations
20.
Galeano, Pedro, et al.. (2000). Multivariate Analysis in Vector Time Series. e-Archivo (Carlos III University of Madrid). 4(4). 383–403. 41 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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