Isabel Molina

1.7k total citations
49 papers, 1.1k citations indexed

About

Isabel Molina is a scholar working on Economics and Econometrics, Statistics and Probability and Sociology and Political Science. According to data from OpenAlex, Isabel Molina has authored 49 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Economics and Econometrics, 19 papers in Statistics and Probability and 17 papers in Sociology and Political Science. Recurrent topics in Isabel Molina's work include Statistical Methods and Bayesian Inference (16 papers), demographic modeling and climate adaptation (15 papers) and Income, Poverty, and Inequality (15 papers). Isabel Molina is often cited by papers focused on Statistical Methods and Bayesian Inference (16 papers), demographic modeling and climate adaptation (15 papers) and Income, Poverty, and Inequality (15 papers). Isabel Molina collaborates with scholars based in Spain, United States and Canada. Isabel Molina's co-authors include J. N. K. Rao, Domingo Morales, Yolanda Marhuenda García, María José Lombardía, Wenceslao González–Manteiga, L. Santamaría, Ayoub Saei, Nicola Salvati, Monica Pratesi and Nirian Martín and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Annals of Statistics and Journal of the Royal Statistical Society Series A (Statistics in Society).

In The Last Decade

Isabel Molina

45 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Isabel Molina Spain 16 564 390 326 303 150 49 1.1k
Ray Chambers Australia 18 464 0.8× 389 1.0× 569 1.7× 202 0.7× 122 0.8× 71 1.2k
Robert E. Fay United States 11 440 0.8× 296 0.8× 686 2.1× 256 0.8× 116 0.8× 21 1.4k
Partha Lahiri United States 19 649 1.2× 607 1.6× 878 2.7× 156 0.5× 211 1.4× 68 1.6k
María José Lombardía Spain 14 315 0.6× 225 0.6× 263 0.8× 104 0.3× 90 0.6× 36 703
Hukum Chandra India 18 301 0.5× 190 0.5× 270 0.8× 113 0.4× 92 0.6× 87 897
Roger A. Herriot United States 4 406 0.7× 276 0.7× 443 1.4× 149 0.5× 111 0.7× 6 930
Rachel Harter United States 8 248 0.4× 198 0.5× 310 1.0× 116 0.4× 76 0.5× 14 666
Raymond L. Chambers Australia 19 195 0.3× 210 0.5× 697 2.1× 131 0.4× 29 0.2× 52 1.1k
L. Santamaría Spain 5 193 0.3× 149 0.4× 111 0.3× 73 0.2× 71 0.5× 10 344
Yolanda Marhuenda García Spain 7 154 0.3× 105 0.3× 90 0.3× 78 0.3× 37 0.2× 15 330

Countries citing papers authored by Isabel Molina

Since Specialization
Citations

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

Fields of papers citing papers by Isabel Molina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Isabel Molina

This figure shows the co-authorship network connecting the top 25 collaborators of Isabel Molina. A scholar is included among the top collaborators of Isabel Molina 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 Isabel Molina. Isabel Molina 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.
Álvarez-Fuente, María, et al.. (2023). Initial experience with the new percutaneous pulmonary self-expandable Venus P-valve. SHILAP Revista de lepidopterología. 1 indexed citations
2.
Morales, Domingo, et al.. (2021). Time stable empirical best predictors under a unit-level model. Computational Statistics & Data Analysis. 160. 107226–107226. 5 indexed citations
3.
Corral, Paul, Kristen Himelein, Kevin McGee, & Isabel Molina. (2021). A Map of the Poor or a Poor Map?. Mathematics. 9(21). 2780–2780. 9 indexed citations
4.
Molina, Isabel. (2020). Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh. SHILAP Revista de lepidopterología. 21(4). 40–44. 2 indexed citations
5.
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
6.
Molina, Isabel, et al.. (2017). Analysis of seasonality in monthly pork prices in the Philippines based on X-12 ARIMA.. 23(2). 215–226. 4 indexed citations
7.
Molina, Isabel, et al.. (2017). Small area estimation of general parameters under complex sampling designs. Computational Statistics & Data Analysis. 121. 20–40. 21 indexed citations
8.
Molina, Isabel, et al.. (2016). A comparison of small area estimation methods for poverty mapping. Statistics in Transition New Series. 17(1). 41–66. 10 indexed citations
9.
Molina, Isabel, et al.. (2015). Isolation and Compositional Analysis of Plant Cuticle Lipid Polyester Monomers. Journal of Visualized Experiments. 13 indexed citations
10.
García, Yolanda Marhuenda, Isabel Molina, & Domingo Morales. (2012). Small area estimation with spatio-temporal Fay–Herriot models. Computational Statistics & Data Analysis. 58. 308–325. 98 indexed citations
11.
Pratesi, Monica, Stefano Marchetti, Nicola Salvati, et al.. (2010). Final Small Area Estimation Developments and Simulation Results. CINECA IRIS Institutial research information system (University of Pisa). 6 indexed citations
12.
Morales, Domingo, María Dolores Esteban, L. Santamaría, et al.. (2009). Estimadores de áreas pequeñas basados en modelos para la Encuesta de Población Activa(. Estadística española. 51(170). 133–172. 1 indexed citations
13.
Baı́llo, Amparo & Isabel Molina. (2009). Mean-squared errors of small-area estimators under a unit-level multivariate model. Statistics. 43(6). 553–569. 6 indexed citations
14.
González–Manteiga, Wenceslao, María José Lombardía, Isabel Molina, Domingo Morales, & L. Santamaría. (2008). Bootstrap mean squared error of a small-area EBLUP. Journal of Statistical Computation and Simulation. 78(5). 443–462. 100 indexed citations
15.
Molina, Isabel. (2008). Uncertainty under a multivariate nested-error regression model with logarithmic transformation. Journal of Multivariate Analysis. 100(5). 963–980. 9 indexed citations
16.
González–Manteiga, Wenceslao, María José Lombardía, Isabel Molina, Domingo Morales, & L. Santamaría. (2006). Estimation of the mean squared error of predictors of small area linear parameters under a logistic mixed model. Computational Statistics & Data Analysis. 51(5). 2720–2733. 81 indexed citations
17.
Molina, Isabel & Domingo Morales. (2005). Rényi statistics for testing hypotheses in mixed linear regression models. Journal of Statistical Planning and Inference. 137(1). 87–102. 3 indexed citations
18.
Molina, Isabel, et al.. (2005). On convergence of fisher informations in continuous models with quantized observations. Test. 14(1). 151–179. 1 indexed citations
19.
Molina, Isabel, et al.. (2004). A comparative study of small area estimators. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 28(2). 215–230. 3 indexed citations
20.
Molina, Isabel, et al.. (2003). Likelihood Divergence Statistics for Testing Hypotheses in Familial Data. Communication in Statistics- Theory and Methods. 32(2). 415–434. 4 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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