Filidor Vilca

763 total citations
40 papers, 567 citations indexed

About

Filidor Vilca is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence. According to data from OpenAlex, Filidor Vilca has authored 40 papers receiving a total of 567 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Statistics and Probability, 13 papers in Statistics, Probability and Uncertainty and 11 papers in Artificial Intelligence. Recurrent topics in Filidor Vilca's work include Statistical Distribution Estimation and Applications (30 papers), Statistical Methods and Bayesian Inference (16 papers) and Advanced Statistical Methods and Models (14 papers). Filidor Vilca is often cited by papers focused on Statistical Distribution Estimation and Applications (30 papers), Statistical Methods and Bayesian Inference (16 papers) and Advanced Statistical Methods and Models (14 papers). Filidor Vilca collaborates with scholars based in Brazil, Chile and Canada. Filidor Vilca's co-authors include Víctor Leiva, N. Balakrishnan, Antonio Sanhueza, Manuel Galea, Víctor H. Lachos, George Christakos, Heleno Bolfarine, N. Balakrishnan, Gilberto A. Paula and Héctor W. Gómez and has published in prestigious journals such as Statistics in Medicine, Computational Statistics & Data Analysis and Journal of Multivariate Analysis.

In The Last Decade

Filidor Vilca

36 papers receiving 554 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Filidor Vilca Brazil 13 506 187 129 71 68 40 567
Michelli Barros Brazil 12 563 1.1× 201 1.1× 127 1.0× 77 1.1× 70 1.0× 20 674
Anthony F. Desmond Canada 9 376 0.7× 205 1.1× 64 0.5× 22 0.3× 29 0.4× 28 491
Sadegh Rezaei Iran 11 344 0.7× 261 1.4× 33 0.3× 37 0.5× 78 1.1× 39 452
Malwane M. A. Ananda United States 11 442 0.9× 194 1.0× 69 0.5× 81 1.1× 72 1.1× 37 580
Eslam Hussam Saudi Arabia 13 426 0.8× 235 1.3× 50 0.4× 52 0.7× 83 1.2× 71 496
Fahimah Al-Awadhi Kuwait 9 260 0.5× 142 0.8× 77 0.6× 41 0.6× 55 0.8× 24 353
Abhimanyu Singh Yadav India 13 402 0.8× 287 1.5× 66 0.5× 35 0.5× 53 0.8× 65 626
Manoel Santos‐Neto Brazil 9 264 0.5× 101 0.5× 57 0.4× 52 0.7× 43 0.6× 21 327
Vera Tomazella Brazil 12 335 0.7× 108 0.6× 62 0.5× 21 0.3× 22 0.3× 59 416
Kahadawala Cooray United States 10 437 0.9× 231 1.2× 80 0.6× 110 1.5× 112 1.6× 19 562

Countries citing papers authored by Filidor Vilca

Since Specialization
Citations

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

Fields of papers citing papers by Filidor Vilca

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Filidor Vilca

This figure shows the co-authorship network connecting the top 25 collaborators of Filidor Vilca. A scholar is included among the top collaborators of Filidor Vilca 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 Filidor Vilca. Filidor Vilca 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.
Bolfarine, Heleno, et al.. (2025). Birnbaum–Saunders Distribution Based on Asymmetric Heavy-Tailed Distributions, Associated Inference, and Application. Mathematical Methods of Statistics. 34(1). 34–53.
2.
Vilca, Filidor, et al.. (2023). Multivariate Birnbaum–Saunders distribution based on a skewed distribution and associated EM-estimation. Brazilian Journal of Probability and Statistics. 37(1). 2 indexed citations
3.
Vilca, Filidor, et al.. (2022). Theoretical results and modeling under the discrete Birnbaum-Saunders distribution. Communication in Statistics- Theory and Methods. 53(5). 1745–1759. 2 indexed citations
4.
Vilca, Filidor, et al.. (2022). Finite Mixture of Birnbaum–Saunders Distributions Using the k-Bumps Algorithm. Journal of Statistical Theory and Practice. 16(2). 2 indexed citations
5.
Bolfarine, Heleno, et al.. (2021). A robust Birnbaum–Saunders regression model based on asymmetric heavy-tailed distributions. Metrika. 84(7). 1049–1080. 2 indexed citations
6.
Azevedo, Caio, et al.. (2020). A log Birnbaum–Saunders regression model based on the skew-normal distribution under the centred parameterization. Statistics and Its Interface. 13(3). 335–346. 1 indexed citations
7.
Vilca, Filidor, et al.. (2018). Diagnostics analysis for skew-normal linear regression models: Applications to a quality of life dataset. Brazilian Journal of Probability and Statistics. 32(3). 3 indexed citations
8.
Vilca, Filidor. (2018). Discussion of Birnbaum‐Saunders distribution: A review of models, analysis, and applications. Applied Stochastic Models in Business and Industry. 35(1). 100–103. 1 indexed citations
9.
Vilca, Filidor, et al.. (2016). Slashed generalized Rayleigh distribution. Communication in Statistics- Theory and Methods. 46(10). 4686–4699. 19 indexed citations
10.
Vilca, Filidor, et al.. (2016). Modified slash Birnbaum-Saunders distribution. Hacettepe Journal of Mathematics and Statistics. 46(112). 1–1. 10 indexed citations
11.
Vilca, Filidor, et al.. (2015). The sinh-normal/independent nonlinear regression model. Journal of Applied Statistics. 42(8). 1659–1676. 4 indexed citations
12.
Lachos, Víctor H., Filidor Vilca, Valéria Troncoso Baltar, et al.. (2015). A mixed‐effect model for positive responses augmented by zeros. Statistics in Medicine. 34(10). 1761–1778. 12 indexed citations
13.
Vilca, Filidor, et al.. (2014). Multivariate Skew-Normal Generalized Hyperbolic distribution and its properties. Journal of Multivariate Analysis. 128. 73–85. 26 indexed citations
14.
Vilca, Filidor, et al.. (2013). A robust extension of the bivariate Birnbaum–Saunders distribution and associated inference. Journal of Multivariate Analysis. 124. 418–435. 17 indexed citations
15.
Galea, Manuel, et al.. (2010). The structural Sharpe model under t -distributions. Journal of Applied Statistics. 37(12). 1979–1990. 5 indexed citations
16.
Lachos, Víctor H., et al.. (2010). Local influence analysis for regression models with scale mixtures of skew-normal distributions. Journal of Applied Statistics. 38(2). 343–368. 24 indexed citations
17.
Balakrishnan, N., Víctor Leiva, Antonio Sanhueza, & Filidor Vilca. (2009). Estimation in the Birnbaum-Saunders distribution based on scale-mixture of normals and the EM-algorithm. Americanae (AECID Library). 33(2). 171–192. 49 indexed citations
18.
Galea, Manuel, José A. Dı́az-Garcı́a, & Filidor Vilca. (2008). Influence diagnostics in the capital asset pricing model under elliptical distributions. Journal of Applied Statistics. 35(2). 179–192. 9 indexed citations
19.
Galea, Manuel, Heleno Bolfarine, & Filidor Vilca. (2005). Local Influence in Comparative Calibration Models Under Ellipticalt -Distributions. Biometrical Journal. 47(5). 691–706. 8 indexed citations
20.
Galea, Manuel, Heleno Bolfarine, & Filidor Vilca. (2002). Influence diagnostics for the structural errors-in-variables model under the Student-t distribution. Journal of Applied Statistics. 29(8). 1191–1204. 10 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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