Smart Visualization of Mixed Data

Autor: Aurea Grané, Giancarlo Manzi, Silvia Salini

Publikováno v: Stats, Vol 4, Iss 2, Pp 472-485 (2021)

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Ve

Externí odkaz: https://doaj.org/article/cd326de855a246c68cc4e2ed5a35db59

Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data

Autor: Amparo Baíllo, Aurea Grané

Publikováno v: Mathematics, Vol 9, Iss 18, p 2247 (2021)

The distance-based linear model (DB-LM) extends the classical linear regression to the framework of mixed-type predictors or when the only available information is a distance matrix between regressors (as it sometimes happens with big data). The main

Externí odkaz: https://doaj.org/article/cff5f5416c35451f952b496195b0e842

How to Explain the Cross-Section of Equity Returns through Common Principal Components

Autor: José Manuel Cueto, Aurea Grané, Ignacio Cascos

Publikováno v: Mathematics, Vol 9, Iss 9, p 1011 (2021)

In this paper, we propose a procedure to obtain and test multifactor models based on statistical and financial factors. A major issue in the factor literature is to select the factors included in the model, as well as the construction of the portfoli

Externí odkaz: https://doaj.org/article/51e2dcc08b8c46bf8eee67890966df3e

Research patterns in communication (2009–2019): testing female representation and productivity differences, within the most cited authors and the field

Autor: Manuel Goyanes, Márton Demeter, Aurea Grané, Tamás Tóth, Homero Gil de Zúñiga

Publikováno v: Scientometrics. 128:137-156

This study compares the share of male/female as first authors, the growth of authors per paper, and the differences in publication productivity in the last decade of the most cited authors versus the field of communication (i.e., a representative sam

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0a160cf5adc303991022231db87274d3
https://doi.org/10.1007/s11192-022-04575-4

Smart Visualization of Mixed Data

Autor: Giancarlo Manzi, Silvia Salini, Aurea Grané

Publikováno v: Stats, Vol 4, Iss 29, Pp 472-485 (2021)
Stats
Volume 4
Issue 2
Pages 29-485
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Ve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0dc8d890210dc8157c2fbbf5b04b97de
https://doi.org/10.3390/stats4020029