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Statistical Inference of Complex Systems

Grant number: 17/25971-0
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): November 01, 2018
Effective date (End): October 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:Francisco Aparecido Rodrigues
Grantee:Pedro Luiz Ramos
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

The purpose of this post-doctoral project is to propose different estimation methods that can be applied in complex networks. Firstly, we will consider Bayesian methods to determine the minimum size of networks so that real network properties are observed as well as the distribution of the number of connections. This study will solve a fundamental problem in networks which are related to the construction of a taxonomy of complex networks. In this case, we will able to determine the main similarities and differences between classes of networks such as social and biological. Another important problem in complex systems is related to the use of power law distributions. The parameter estimators of such distributions have been discussed earlier under the maximum likelihood estimators. However, different estimation procedures, as well as, Bayesian methods may return better estimates, especially for small samples. Therefore, we will develop new tools to obtain the parameter estimates of power law distributions. In this project, we will also explore regression methods to quantify the relationship between the dynamic structure of complex networks. The aim is to quantify how local properties of the vertices can be used to predict dynamic properties, such as the oscillator synchronization level. In this case, the challenge lies in the fact that the observations are not independent and, therefore, sophisticated Bayesian methods need be considered, for instance, models using a regression structure with coupling functions. Finally, we will introduce a new estimation procedure based on a modification of the maximum likelihood estimators that allow us to obtain closed-form estimators. For this new method, sufficient and necessary conditions will be studied to obtain its asymptotic properties.

Articles published in other media outlets (1 total):
Saense: Modelo estatístico mostra atribulada vida dos imperadores romanos (12/Apr/2021)

Scientific publications (14)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MORITA, LIA H. M.; TOMAZELLA, VERA L.; BALAKRISHNAN, NARAYANASWAMY; RAMOS, PEDRO L.; FERREIRA, PAULO H.; LOUZADA, FRANCISCO. Inverse Gaussian process model with frailty term in reliability analysis. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, v. 37, n. 2, p. 763-784, MAR 2021. Web of Science Citations: 0.
DE ALMEIDA, MARCELLO HENRIQUE; RAMOS, PEDRO LUIZ; RAO, GADDE SRINIVASA; MOALA, FERNANDO ANTONIO. Objective Bayesian inference for the capability index of the Gamma distribution. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, FEB 2021. Web of Science Citations: 0.
MORITA, LIA H. M.; TOMAZELLA, VERA L. D.; RAMOS, PEDRO L.; FERREIRA, PAULO H.; LOUZADA, FRANCISCO. The random deterioration rate model with measurement error based on the inverse Gaussian distribution. BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS, v. 35, n. 1, p. 187-204, FEB 2021. Web of Science Citations: 0.
MORITA, LIA H. M.; TOMAZELLA, VERA L.; FERREIRA, PAULO H.; RAMOS, PEDRO L.; BALAKRISHNAN, NARAYANASWAMY; LOUZADA, FRANCISCO. Optimal burn-in policy based on a set of cutoff points using mixture inverse Gaussian degradation process and copulas. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, JAN 2021. Web of Science Citations: 0.
FERREIRA, PAULO H.; RAMOS, EDUARDO; RAMOS, PEDRO L.; GONZALES, JHON F. B.; TOMAZELLA, VERA L. D.; EHLERS, RICARDO S.; SILVA, EVELINY B.; LOUZADA, FRANCISCO. Objective Bayesian analysis for the Lomax distribution. Statistics & Probability Letters, v. 159, APR 2020. Web of Science Citations: 0.
CHESNEAU, CHRISTOPHE; BAKOUCH, HASSAN S.; RAMOS, PEDRO L.; LOUZADA, FRANCISCO. The polynomial-exponential distribution: a continuous probability model allowing for occurrence of zero values. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, MAR 2020. Web of Science Citations: 0.
DIEGO CARVALHO DO NASCIMENTO; PEDRO LUIZ RAMOS; ANDRÉ ENNES; CAMILA COCOLO; MÁRCIO JOSÉ NICOLA; CARLOS ALONSO; LUIZ GUSTAVO RIBEIRO; FRANCISCO LOUZADA. A reliability engineering case study of sugarcane harvesters. Gestão & Produção, v. 27, n. 4, p. -, 2020.
TOMAZELLA, VERA L. D.; DE JESUS, SANDRA R.; LOUZADA, FRANCISCO; NADARAJAH, SARALEES; RAMOS, PEDRO L. Reference Bayesian analysis for the generalized lognormal distribution with application to survival data. STATISTICS AND ITS INTERFACE, v. 13, n. 1, p. 139-149, 2020. Web of Science Citations: 0.
NASCIMENTO, DIEGO C.; BARBOSA, BRUNO; PEREZ, ANDRE M.; CAIRES, DANIEL O.; HIRAMA, EDGAR; RAMOS, PEDRO L.; LOUZADA, FRANCISCO. Risk Management in E-Commerce-A Fraud Study Case Using Acoustic Analysis through Its Complexity. Entropy, v. 21, n. 11 NOV 2019. Web of Science Citations: 0.
RAMOS, PEDRO L.; LOUZADA, FRANCISCO; RAMOS, EDUARDO; DEY, SANKU. The Frechet distribution: Estimation and application-An overview. JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS, NOV 2019. Web of Science Citations: 0.
RAMOS, PEDRO L.; DEY, DIPAK K.; LOUZADA, FRANCISCO; LACHOS, VICTOR H. An extended poisson family of life distribution: a unified approach in competitive and complementary risks. Journal of Applied Statistics, v. 47, n. 2 JULY 2019. Web of Science Citations: 1.
RAMOS, PEDRO LUIZ; LOUZADA, FRANCISCO. A note on the exponential geometric power series distribution. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, JUN 2019. Web of Science Citations: 0.
SABOOR, ABDUS; KHAN, MUHAMMAD NAUMAN; CORDEIRO, GAUSS M.; PASCOA, MARCELINO A. R.; RAMOS, PEDRO L.; KAMAL, MUSTAFA. Some new results for the transmuted generalized gamma distribution. Journal of Computational and Applied Mathematics, v. 352, p. 165-180, MAY 15 2019. Web of Science Citations: 0.
P. L. RAMOS; D. C. NASCIMENTO; R. FERNANDES; E. GUIMARÃES; M. SANTANA; K. SOARES; F. LOUZADA. Medical Care in Emergency Units with Risk Classification: Time to Attendance at a Hospital based on Parametric Models. TEMA (São Carlos), v. 20, n. 3, p. 571-585, Dez. 2019.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.