Leonardo Rojas‐Nandayapa

457 total citations
19 papers, 260 citations indexed

About

Leonardo Rojas‐Nandayapa is a scholar working on Management Science and Operations Research, Finance and Statistics and Probability. According to data from OpenAlex, Leonardo Rojas‐Nandayapa has authored 19 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Management Science and Operations Research, 9 papers in Finance and 9 papers in Statistics and Probability. Recurrent topics in Leonardo Rojas‐Nandayapa's work include Probability and Risk Models (13 papers), Financial Risk and Volatility Modeling (9 papers) and Statistical Distribution Estimation and Applications (6 papers). Leonardo Rojas‐Nandayapa is often cited by papers focused on Probability and Risk Models (13 papers), Financial Risk and Volatility Modeling (9 papers) and Statistical Distribution Estimation and Applications (6 papers). Leonardo Rojas‐Nandayapa collaborates with scholars based in Australia, Denmark and United States. Leonardo Rojas‐Nandayapa's co-authors include Søren Asmussen, Jens Ledet Jensen, José Blanchet, Sandeep Juneja, Mogens Bladt, Dirk P. Kroese, Sergey Foss, Zdravko I. Botev, Ad Ridder and Thomas S. Salisbury and has published in prestigious journals such as Annals of Operations Research, Political Geography and Journal of Applied Probability.

In The Last Decade

Leonardo Rojas‐Nandayapa

17 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Leonardo Rojas‐Nandayapa Australia 7 123 105 70 37 31 19 260
Łukasz Stettner Poland 8 143 1.2× 142 1.4× 82 1.2× 7 0.2× 34 1.1× 16 308
Xiaoli Wei China 8 50 0.4× 131 1.2× 24 0.3× 21 0.6× 19 0.6× 20 252
Hans Rudolf Lerche Germany 11 82 0.7× 188 1.8× 67 1.0× 5 0.1× 33 1.1× 19 318
Naci Saldı Türkiye 10 64 0.5× 82 0.8× 17 0.2× 19 0.5× 6 0.2× 32 202
Zhengyan Lin China 12 178 1.4× 220 2.1× 198 2.8× 14 0.4× 29 0.9× 63 498
Wensheng Wang China 10 81 0.7× 136 1.3× 33 0.5× 12 0.3× 33 1.1× 64 303
N. Balakrishna India 10 35 0.3× 98 0.9× 131 1.9× 17 0.5× 11 0.4× 43 246
Elke Korn Germany 7 63 0.5× 169 1.6× 19 0.3× 12 0.3× 41 1.3× 8 247
W.P. Malcolm Australia 9 100 0.8× 248 2.4× 27 0.4× 8 0.2× 41 1.3× 31 390
Mathieu Rosenbaum France 10 53 0.4× 240 2.3× 92 1.3× 8 0.2× 13 0.4× 33 417

Countries citing papers authored by Leonardo Rojas‐Nandayapa

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Rojas‐Nandayapa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leonardo Rojas‐Nandayapa. 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 Leonardo Rojas‐Nandayapa. The network helps show where Leonardo Rojas‐Nandayapa may publish in the future.

Co-authorship network of co-authors of Leonardo Rojas‐Nandayapa

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Rojas‐Nandayapa. A scholar is included among the top collaborators of Leonardo Rojas‐Nandayapa 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 Leonardo Rojas‐Nandayapa. Leonardo Rojas‐Nandayapa 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.
Bladt, Mogens & Leonardo Rojas‐Nandayapa. (2018). Fitting phase–type scale mixtures to heavy–tailed data and distributions. Extremes. 21(2). 285–313. 10 indexed citations
2.
Andersen, Lars Nørvang, et al.. (2017). Efficient Simulation for Dependent Rare Events with Applications to Extremes. Methodology And Computing In Applied Probability. 20(1). 385–409. 2 indexed citations
3.
Rojas‐Nandayapa, Leonardo, et al.. (2017). Asymptotic tail behaviour of phase-type scale mixture distributions. Annals of Actuarial Science. 12(2). 412–432. 6 indexed citations
4.
Rojas‐Nandayapa, Leonardo, et al.. (2017). Approximation of ruin probabilities via Erlangized scale mixtures. Insurance Mathematics and Economics. 78. 136–156. 5 indexed citations
5.
Rojas‐Nandayapa, Leonardo, et al.. (2016). Estimating tail probabilities of random sums of infinite mixtures of phase-type distributions. Winter Simulation Conference. 347–358.
6.
Botev, Zdravko I., Ad Ridder, & Leonardo Rojas‐Nandayapa. (2016). Semiparametric cross entropy for rare-event simulation. Journal of Applied Probability. 53(3). 633–649. 3 indexed citations
7.
Rojas‐Nandayapa, Leonardo, et al.. (2016). Estimating tail probabilities of random sums of infinite mixtures of phase-type distributions. 2016 Winter Simulation Conference (WSC). 3. 347–358.
8.
Asmussen, Søren, Jens Ledet Jensen, & Leonardo Rojas‐Nandayapa. (2016). Exponential Family Techniques for the Lognormal Left Tail. Scandinavian Journal of Statistics. 43(3). 774–787. 19 indexed citations
9.
Asmussen, Søren, Jens Ledet Jensen, & Leonardo Rojas‐Nandayapa. (2014). On the Laplace Transform of the Lognormal Distribution. Methodology And Computing In Applied Probability. 18(2). 441–458. 62 indexed citations
10.
Nazarathy, Yoni, Leonardo Rojas‐Nandayapa, & Thomas S. Salisbury. (2013). Non-existence of stabilizing policies for the critical push–pull network and generalizations. Operations Research Letters. 41(3). 265–270. 2 indexed citations
11.
Blanchet, José & Leonardo Rojas‐Nandayapa. (2011). Efficient simulation of tail probabilities of sums of dependent random variables. Journal of Applied Probability. 48(A). 147–164. 5 indexed citations
12.
Rojas‐Nandayapa, Leonardo, Sergey Foss, & Dirk P. Kroese. (2011). Stability and performance of greedy server systems. Queueing Systems. 68(3-4). 221–227. 12 indexed citations
13.
Blanchet, José & Leonardo Rojas‐Nandayapa. (2011). Efficient simulation of tail probabilities of sums of dependent random variables. Journal of Applied Probability. 48(A). 147–164. 1 indexed citations
14.
Asmussen, Søren, José Blanchet, Sandeep Juneja, & Leonardo Rojas‐Nandayapa. (2009). Efficient simulation of tail probabilities of sums of correlated lognormals. Annals of Operations Research. 189(1). 5–23. 37 indexed citations
15.
Blanchet, José, Sandeep Juneja, & Leonardo Rojas‐Nandayapa. (2008). Efficient tail estimation for sums of correlated lognormals. 2008 Winter Simulation Conference. 12. 607–614. 6 indexed citations
16.
Asmussen, Søren & Leonardo Rojas‐Nandayapa. (2008). Asymptotics of sums of lognormal random variables with Gaussian copula. Statistics & Probability Letters. 78(16). 2709–2714. 74 indexed citations
17.
Blanchet, José, Sandeep Juneja, & Leonardo Rojas‐Nandayapa. (2008). Efficient tail estimation for sums of correlated lognormals. 607–614. 6 indexed citations
18.
Rojas‐Nandayapa, Leonardo & Søren Asmussen. (2007). Efficient simulation of finite horizon problems in queueing and insurance risk. Queueing Systems. 57(2-3). 85–97. 1 indexed citations
19.
Rojas‐Nandayapa, Leonardo, et al.. (2004). Spatial and temporal effects in Mexican direct elections for the chamber of deputies. Political Geography. 23(5). 529–548. 9 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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