Advances in Probability and Mathematical Statistics
CLAPEM 2019, Mérida, Mexico
1st Edition
Published on 14. November 2021
IX, 174 pages
978-3-030-85325-9 (ISBN)
Description
This volume contains papers which were presented at the XV Latin American Congress of Probability and Mathematical Statistics (CLAPEM) in December 2019 in Mérida-Yucatán, México. They represent well the wide set of topics on probability and statistics that was covered at this congress, and their high quality and variety illustrates the rich academic program of the conference.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
IX, 174 p. 14 illus., 12 illus. in color.
File size
3,37 MB
ISBN-13
978-3-030-85325-9 (9783030853259)
DOI
10.1007/978-3-030-85325-9
Schweitzer Classification
Other editions
Additional editions
Daniel Hernández-Hernández | Florencia Leonardi | Ramsés H. Mena
Advances in Probability and Mathematical Statistics
CLAPEM 2019, Mérida, Mexico
Book
11/2021
Birkhäuser
€171.19
Shipment within 7-9 days
Persons
Daniel Hernández Hernández
is a researcher at CIMAT, Guanajuato, Mexico. He is also the academic coordinator of CIMAT.
Florencia Leonardi
is a researcher in probability and statistics at the University of Sao Paulo, Brazil. She is also the former president of the SLAPEM
Ramsés H. Mena
is a researcher in statistics at UNAM, Mexico. He is also the director of IIMAS at UNAM.
Juan Carlos Pardo Millán
is a researcher in probability at CIMAT, Guanajuato, Mexico. He is also the current president of the SLAPEM
Content
- Intro
- Preface
- Contents
- Asymptotic Connectedness of Random Interval Graphs in a One Dimensional Data Delivery Problem
- 1 Introduction
- 1.1 The Data Delivery on a Line Problem
- 1.2 Computational Complexity
- 1.3 Graphs
- 1.4 Random Interval Graphs
- 2 Graph Theoretical Formulation of the Data Delivery on a Line Problem
- 3 Probabilistic Analysis of GA(Q,a)
- 4 Simulations
- 5 Conclusion
- References
- Geometric Random Graphs on Circles
- 1 Introduction and Main Results
- 2 Definitions and Notation
- 3 Distinguishing Graphs Arising from Different L
- 4 Recovering Distances and Non-isomorphism for Irrational L
- 4.1 Proof of Proposition 4.1
- 4.2 Proof of Lemma 4.4
- 4.2.1 Proof of Lemma 4.4 when L =3
- 4.2.2 Proof of Lemma 4.4 when 2&L&3
- 5 Constructing an Isomorphism for Rational L
- References
- More About Asymptotic Properties of Some Binary Classification Methods for High Dimensional Data
- 1 Introduction
- 2 Asymptotic Behavior of Binary Classification Methods
- 2.1 Angle Between the Normal Vector and the Optimal Direction
- 2.2 Probability of Misclassification
- 3 Simulation Study
- 4 Conclusions
- Appendix
- Proof of Theorem 2.1
- The Data in the Simulations Satisfy Conditions (1)-(4)
- References
- Transport Distances on Random Vectors of Measures: Recent Advances in Bayesian Nonparametrics
- 1 Introduction
- 2 Completely Random Vectors
- 3 Transport Distances
- 4 Measuring Dependence in Bayesian Nonparametrics
- 5 Survival Analysis in Bayesian Nonparametrics
- 6 Time-Dependent Kernels
- 7 Discussion and Further Work
- References
- Bayesian Non-parametric Priors Based on Random Sets
- 1 Introduction
- 2 Distributions on the Simplex Based on Distributions of Random Sets
- 2.1 Density Estimation Scheme
- 3 Examples
- 3.1 Random Sets with No Gaps
- 3.2 Random Sets with Bernoulli Chosen Elements
- 4 Geometric Product Stick-Breaking Processes
- 4.1 Illustrations
- 5 Final Remarks
- Appendix
- References
- Conjugate Predictive Distributions and Generalized Entropies
- 1 Introduction
- 2 A Family of Divergences
- 3 Main Idea
- 3.1 Student's t Distribution
- 3.2 Pareto Type II Distribution
- 4 Discussion
- References
- Approximation and Mean Field Control of Systems of LargePopulations
- 1 Introduction
- 2 The N-Objects Markov Control Model
- 2.1 Formulation of the N-Markov Control Model (N-MCM)
- 2.2 Optimality in the N-MCM
- 3 The Mean Field Control Model
- 3.1 Approximation via Bounded Costs
- 4 Proofs
- 4.1 Proof of Theorem 3.1
- 4.2 Proof of Theorem 3.4
- 4.3 Proof of Theorem 3.5
- References
- Explicit Solution Simulation Method for the 3/2 Model
- 1 Introduction
- 2 Setting and Main Results
- 3 Pricing Algorithm
- 3.1 Importance Sampling Estimator of the Option Price
- 3.2 Simulating Sample Paths
- 4 Numerical Experiment
- 4.1 Methods and Parameters
- 4.2 Results
- 4.2.1 Sensitivity to n
- 4.2.2 Comparison to Other Algorithms
- 5 Conclusion
- Appendix
- References
- Large Deviations and Wschebor's Theorems
- 1 Introduction: Wschebor's Theorem and Beyond
- 2 The a-Stable Lévy Process
- 2.1 LDP for (µS?)
- 2.2 LDP for (MS?)
- 3 The Fractional Brownian Motion
- 3.1 General Statement
- 3.2 Contraction
- 3.3 Particular Cases
- 3.3.1 Remark: Two Basic Mollifiers
- 3.3.2 Looking for an Explicit Rate Function
- 4 ``Level Process'' Study
- 5 Discretization and Random Walks
- 5.1 Fixed Lag
- 5.2 Unbounded Lag
- References
