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Selected Contributions on Statistics and Data Science in Latin America

33 FNE and 13 CLATSE, 2018, Guadalajara, Mexico, October 1−5

  • Isadora Antoniano-Villalobos
  • Ramsés H. Mena
  • Manuel Mendoza
  • Lizbeth Naranjo
  • Luis E. Nieto-Barajas
Conference proceedings FNE 2018

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Michelle Anzarut, Luis Felipe González, María Teresa Ortiz
    Pages 1-13
  3. F. Baltazar-Larios, Luz Judith R. Esparza
    Pages 15-31
  4. Sergio A. Bauz-Olvera, Johny J. Pambabay-Calero, Ana B. Nieto-Librero, Ma. Purificación Galindo-Villardón
    Pages 33-42
  5. Arrigo Coen, Beatriz Godínez-Chaparro
    Pages 43-58
  6. Asael Fabian Martínez
    Pages 69-80
  7. Francisco Novoa-Muñoz, Sergio Contreras Espinoza, Aníbal Coronel Pérez, Ian Hess Duque
    Pages 95-110
  8. Eduardo Pérez Castro, Flaviano Godínez Jaimes, Elia Barrera Rodríguez, Ramón Reyes Carreto, Raúl López Roque, Virginia Vera Leyva
    Pages 111-125
  9. E. I. Velazquez Richards, E. Gallagher, P. Suárez-Serrato
    Pages 145-154

About these proceedings

Introduction

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. 

Keywords

Bayesian Computing Bayesian Inference for Big Data Bayesian Nonparametric Methods Computational Methods for Bayesian Inference Environmental Statistics Spatial Statistics Variational Bayesian Inference

Editors and affiliations

  • Isadora Antoniano-Villalobos
    • 1
  • Ramsés H. Mena
    • 2
  • Manuel Mendoza
    • 3
  • Lizbeth Naranjo
    • 4
  • Luis E. Nieto-Barajas
    • 5
  1. 1.Department of Environmental Sciences, Informatics and StatisticsCa' Foscari University of VeniceVeniceItaly
  2. 2.Department of Probability and StatisticsIIMAS, UNAMMexico CityMexico
  3. 3.Department of StatisticsInstituto Tecnológico Autónomo de MéxicoMexico CityMexico
  4. 4.Department of MathematicsFacultad de Ciencias, UNAMMexico CityMexico
  5. 5.Department of StatisticsInstituto Tecnológico Autónomo de MéxicoMexico CityMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-31551-1
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-31550-4
  • Online ISBN 978-3-030-31551-1
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017
  • Buy this book on publisher's site