Details

Autor(en) / Beteiligte

• Antoniano-Villalobos, Isadora[Herausgeber]
• Mena, Ramsés H[Herausgeber]
• Mendoza, Manuel[Herausgeber]
• Naranjo, Lizbeth[Herausgeber]
• Nieto-Barajas, Luis E[Herausgeber]

Titel

Selected Contributions on Statistics and Data Science in Latin America : 33 FNE and 13 CLATSE, 2018, Guadalajara, Mexico, October 1−5 [electronic resource]

Auflage

1st ed. 2019

Ort / Verlag

Cham : Springer International Publishing

Erscheinungsjahr

2019

Link zum Volltext

• SpringerLink Books - AutoHoldings

Beschreibungen/Notizen

• Anzarut, M., González, L. F., and Ortiz, M. T: A Heavy-tailed Multilevel Mixture Model for the Quick Count in the Mexican Elections of 2018 -- Baltazar, F. and Esparza, L. J. R: Bayesian estimation for the Markov-Modulated Diffusion Risk Model -- Sergio, A. B., Johny, J. P., Ana, B. N., and Purificaci´on, G: Meta-analysis in DTA with hierarchical models Bivariate and HSROC: Simulation study -- Coen, A. and Chaparro, B. G: Compound Dirichlet Processes -- Guti´errez, E. and Walker, S. G: An efficient method to determine the degree of overlap of two multivariate distributions -- Mart´ınez, A. F.: Clustering via non-symmetric partition distributions -- Naranjo, L., Fuentes, R., and P´erez, C, J: A Flexible Replication-Based Classification Approach for Parkinson’s Disease Detection by Using Voice Recordings -- Novoa, F., Espinoza, S. C., P´erez, A. C., and Duque, I. H: Calibration of population growth mathematical models by using time series -- Castro, E. P., Jaimes, F. G., Rodr´ıguez, E. B., Carreto, R. R., Roque, R. L., Leyva, V. V: Impact of the Red Code process using structural equation models -- Esparza, L. J. R.: On a construction of stationary processes via Bilateral Matrix-Exponential distributions -- Richards, E. I. V., Gallagher, E., and Su´arez, P: BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala.
• The volume includes a collection of peer-reviewed contributions from among those presented at the main conference organized yearly by the Mexican Statistical Association (AME) and every two years by a Latin-American Confederation of Statistical Societies. For the 2018 edition, particular attention was placed on the analysis of highly complex or large data sets, which have come to be known as “big data”. Statistical research in Latin America is prolific and research networks span within and outside the region. The goal of this volume is to provide access to selected works from Latin-American collaborators and their research networks to a wider audience. New methodological advances, motivated in part by the challenges of a data-driven world and the Latin American context, will be of interest to academics and practitioners around the world. .