Bachelor in Mathematics from Universidade de São Paulo (1963), master's at Graduate Course in Statistics from University of California, Berkeley (1971) and doctorate at Graduate Course in Statistics from University of California, Berkeley (1972). Has experience in Probability and Statistics, focusing on Probability and Statistics, acting on the following subjects: time series analysis, wavelet analysis and applications in Finance, Actuarial Sciences, Physical Sciences and Biomedical Sciences. (Source: Lattes Curriculum)
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This project is a natural follow-up to an ongoing project 18/04654-9, which is the most recent of a series of thematic projects from this research group endowed by FAPESP in the last twenty years. The proposed statistical methodologies will have application in Medicine, Finance, Biology, Physics, Neurosciences, Engineering, among others areas. They solve theoretical and empirical question…
This project is a natural follow-up to the thematic project 13/00506-1 entitled Séries Temporais, Ondaletas e Análise de Dados Funcionais (Time Series, Wavelets and Functional data Analysis), from 01/07/2013 to 30/06/2018. The methodologies have potential and effective application to Medicine, Biology, Physics, Chemistry, Finance, Engineering, etc. These methods should solve theoretical a…
In this project we will investigate methodologies of time series, wavewlets and functional data analysis, with potential applications in several fields, as medicine, biology, physical sciences, chemistry, actuarial sciences, finance, etc. These methodologies aim to solve some important theoretical and applied problems related to the following strongly connected research topics: 1. Evaluat…
In this project, we will investigate methodologies of time series, measuresof dependence, copulas and new alternative functions of dependence, with po-tential applications in several fields, as Actuarial Sciences (Insurance, PensionFunds, etc), Finance, Medicine, Biology and Physical Sciences.These methodologies aim to solve some important theoretical and appliedproblems related to the fo…
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Factor Models are extensively used as a resource to reduce the dimensionality (number of variables) and to explain the common variability in multivariate data sets. The idea, like in Principal Component analysis, is to consider a small set of factors, which explain most of the variance in the data. In the context of multivariate time series, the analysis of the covariance structure become…
Stock prices usually behave as random walks and ARMA-APARCH models are usually fitted to the log returns due to their heterocesdasticity. Forecasts for different horizons with confidence intervals will be based on APARCH models and exponential smoothing techniques and, without intervals, using neural networks, as the LSTM. Such forecasts will be used to identify trends of growth and fall …
The growing necessity of analyzing large data sets raises the problem of developing strategies to make statistical analyses in the age of Big Data. A very popular method in statistical analyses are regression models, technique used when explanatory variables are used to model some response variable of interest. When the number of explanatory variables is too large compared to sample size,…
The plan consists in developing two methodologies for the problem of deformation in spacial temporal time series. One based in a model involving the spatial correlation function, using expansions in bases of functions, and the other, expanding directly the mapping frm the space to a proper set. (AU)
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| 1 / 1 | Ongoing research grants |
| 13 / 5 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 6 / 6 | Completed scholarships in Brazil |
| 22 / 14 | All research grants and scholarships |
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