Nirian Martín

729 total citations
56 papers, 410 citations indexed

About

Nirian Martín is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Management Science and Operations Research. According to data from OpenAlex, Nirian Martín has authored 56 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Statistics and Probability, 10 papers in Statistics, Probability and Uncertainty and 7 papers in Management Science and Operations Research. Recurrent topics in Nirian Martín's work include Advanced Statistical Methods and Models (40 papers), Statistical Methods and Inference (30 papers) and Statistical Distribution Estimation and Applications (25 papers). Nirian Martín is often cited by papers focused on Advanced Statistical Methods and Models (40 papers), Statistical Methods and Inference (30 papers) and Statistical Distribution Estimation and Applications (25 papers). Nirian Martín collaborates with scholars based in Spain, Canada and India. Nirian Martín's co-authors include Leandro Pardo, Abhijit Mandal, Ayanendranath Basu, N. Balakrishnan, K. Zografos, Isabel Molina, Abhik Ghosh, Yi Li, Lajos Horváth and Domingo Morales and has published in prestigious journals such as IEEE Transactions on Information Theory, The Annals of Statistics and Journal of Computational and Applied Mathematics.

In The Last Decade

Nirian Martín

54 papers receiving 401 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nirian Martín Spain 11 335 90 62 45 34 56 410
Omid Kharazmi Iran 12 262 0.8× 108 1.2× 51 0.8× 24 0.5× 71 2.1× 58 360
Halim Zeghdoudi Algeria 10 310 0.9× 171 1.9× 60 1.0× 25 0.6× 58 1.7× 55 368
Eslam Hussam Saudi Arabia 13 426 1.3× 235 2.6× 77 1.2× 51 1.1× 50 1.5× 71 496
Isha Bagai India 9 253 0.8× 83 0.9× 97 1.6× 54 1.2× 61 1.8× 13 300
Gianna Agrò Italy 7 192 0.6× 102 1.1× 26 0.4× 18 0.4× 83 2.4× 13 338
Abdullah M. Almarashi Saudi Arabia 14 399 1.2× 260 2.9× 58 0.9× 84 1.9× 50 1.5× 55 468
E. I. Abdul Sathar India 10 288 0.9× 167 1.9× 16 0.3× 28 0.6× 47 1.4× 54 321
Catherine Huber‐Carol France 5 215 0.6× 117 1.3× 17 0.3× 96 2.1× 37 1.1× 6 354
Nickos Papadatos Greece 10 199 0.6× 44 0.5× 71 1.1× 13 0.3× 53 1.6× 31 276
Federico J. O'Reilly Mexico 11 213 0.6× 85 0.9× 39 0.6× 12 0.3× 75 2.2× 34 292

Countries citing papers authored by Nirian Martín

Since Specialization
Citations

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

Fields of papers citing papers by Nirian Martín

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nirian Martín

This figure shows the co-authorship network connecting the top 25 collaborators of Nirian Martín. A scholar is included among the top collaborators of Nirian Martín 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 Nirian Martín. Nirian Martín 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.
Gil, Marı́a Ángeles, et al.. (2022). Trends in Mathematical, Information and Data Sciences. Studies in systems, decision and control. 5 indexed citations
2.
Ghosh, Abhik, et al.. (2020). Robust semiparametric inference for polytomous logistic \nregression with complex survey design. Library Open Repository (Universidad Complutense Madrid). 6 indexed citations
3.
Martín, Nirian, et al.. (2020). Robust Wald-type tests based on minimum Rényi pseudodistance estimators for the multiple linear regression model. Journal of Statistical Computation and Simulation. 90(14). 2655–2680. 2 indexed citations
4.
Molina, Isabel & Nirian Martín. (2018). Empirical best prediction under a nested error model with log transformation. The Annals of Statistics. 46(5). 23 indexed citations
5.
Martín, Nirian, et al.. (2017). Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator. Entropy. 20(1). 18–18. 10 indexed citations
6.
Ghosh, Abhik, Abhijit Mandal, Nirian Martín, & Leandro Pardo. (2016). Influence analysis of robust Wald-type tests. Journal of Multivariate Analysis. 147. 102–126. 25 indexed citations
7.
Basu, Ayanendranath, Abhijit Mandal, Nirian Martín, & Leandro Pardo. (2015). Generalized Wald-type tests based on minimum density power divergence estimators. Statistics. 50(1). 1–26. 34 indexed citations
8.
Martín, Nirian. (2015). Diagnostics in a simple correspondence analysis model: An approach based on Cook’s distance for log-linear models. Journal of Multivariate Analysis. 136. 175–189. 3 indexed citations
9.
Balakrishnan, N., Nirian Martín, & Leandro Pardo. (2014). Empirical phi-divergence test statistics for testing simple and composite null hypotheses. Statistics. 49(5). 951–977. 3 indexed citations
10.
Martín, Nirian & Leandro Pardo. (2014). New Influence Measures in Polytomous Logistic Regression Models Based on Phi-Divergence Measures. Communication in Statistics- Theory and Methods. 43(10-12). 2311–2321. 3 indexed citations
11.
Martín, Nirian & Leandro Pardo. (2014). Comments on: Extensions of some classical methods in change point analysis. Test. 23(2). 279–282. 2 indexed citations
12.
Horváth, Lajos, et al.. (2013). Change-point detection in multinomial data using phi-divergence test statistics. Journal of Multivariate Analysis. 118. 53–66. 9 indexed citations
13.
Martín, Nirian & N. Balakrishnan. (2013). Hypothesis testing in a generic nesting framework for general distributions. Journal of Multivariate Analysis. 118. 1–23. 3 indexed citations
14.
Basu, Ayanendranath, Abhijit Mandal, Nirian Martín, & Leandro Pardo. (2012). Testing statistical hypotheses based on the density power divergence. Annals of the Institute of Statistical Mathematics. 65(2). 319–348. 28 indexed citations
15.
16.
Martín, Nirian & Yi Li. (2011). A new class of minimum power divergence estimators with applications to cancer surveillance. Journal of Multivariate Analysis. 102(8). 1175–1193. 2 indexed citations
17.
Martín, Nirian & Leandro Pardo. (2010). Fitting DNA sequences through log-linear modelling with linear constraints. Statistics. 45(6). 605–621. 3 indexed citations
18.
Pardo, Leandro & Nirian Martín. (2010). On the comparison of the pre-test and shrinkage phi-divergence test estimators for the symmetry model of categorical data. Journal of Computational and Applied Mathematics. 235(5). 1160–1179.
19.
Pardo, Leandro & Nirian Martín. (2009). Homogeneity/Heterogeneity Hypotheses for Standardized Mortality Ratios Based on Minimum Power‐divergence Estimators. Biometrical Journal. 51(5). 819–836. 8 indexed citations
20.
Martín, Nirian & Leandro Pardo. (2006). Choosing the best $\phi$-divergence goodness-of-fit statistic in multinomial sampling with linear constraints. Kybernetika. 42(6). 711–722. 1 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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