53 citations · 67 across the 15 of their papers we have counts for
15 papers
Wavelet estimation of nonstationary spatial covariance function
Yangyang Chen, Pedro Alberto Morettin, Ronaldo Dias +1
This work proposes a new procedure for estimating the non-stationary spatial covariance function for Spatial-Temporal Deformation. The proposed procedure is based on a monotonic fu…
Bayesian Adaptive Selection of Basis Functions for Functional Data Representation
Pedro Henrique T. O. Sousa, Camila P. E. de Souza, Ronaldo Dias
Considering the context of functional data analysis, we developed and applied a new Bayesian approach via Gibbs sampler to select basis functions for a finite representation of fun…
Clustering Functional Data via Variational Inference
Chengqian Xian, Camila de Souza, John Jewell +1
Functional data analysis deals with data recorded densely over time (or any other continuum) with one or more observed curves per subject. Conceptually, functional data are continu…
Variational Inference for Bayesian Bridge Regression
Carlos Tadeu Pagani Zanini, Helio dos Santos Migon, Ronaldo Dias
We study the implementation of Automatic Differentiation Variational inference (ADVI) for Bayesian inference on regression models with bridge penalization. The bridge approach uses…
Modeling the Evolution of Infectious Diseases with Functional Data Models: The Case of COVID-19 in Brazil
Julian A. A. Collazos, Ronaldo Dias, Marcelo C. Medeiros
In this paper, we apply statistical methods for functional data to explain the heterogeneity in the evolution of number of deaths of Covid-19 over different regions. We treat the c…
Variational Full Bayes Lasso: Knots Selection in Regression Splines
Larissa Alves, Ronaldo Dias, Helio S. Migon
We develop a fully automatic Bayesian Lasso via variational inference. This is a scalable procedure for approximating the posterior distribution. Special attention is driven to the…
A Basis Approach to Surface Clustering
Adriano Zanin Zambom, Qing Wang, Ronaldo Dias
This paper presents a novel method for clustering surfaces. The proposal involves first using basis functions in a tensor product to smooth the data and thus reduce the dimension t…
Scalable modeling of nonstationary covariance functions with non-folding B-spline deformation
Ronaldo Dias, Guilherme Ludwig, Paul Sampson
We propose a method for nonstationary covariance function modeling, based on the spatial deformation method of Sampson and Guttorp [1992], but using a low-rank, scalable deformatio…
Selection of the Number of Clusters in Functional Data Analysis
Adriano Zanin Zambom, Julian A. Collazos, Ronaldo Dias
Identifying the number of clusters in a dataset is one of the most difficult problems in clustering analysis. A choice of that correctly characterizes the features of the d…
Consistent Variable Selection for Functional Regression Models
Julian A. A. Collazos, Adriano Z. Zambom
The dual problem of testing the predictive significance of a particular covariate, and identification of the set of relevant covariates is common in applied research and methodolog…
Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption
Amanda Lenzi, Camila P. E. de Souza, Ronaldo Dias +2
Understanding the energy consumption patterns of different types of consumers is essential in any planning of energy distribution. However, obtaining consumption information for si…
Aggregated functional data model for Near-Infrared Spectroscopy calibration and prediction
Ronaldo Dias, Nancy L. Garcia, Guilherme Ludwig +1
Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the…
Genetic Algorithm for Constrained Optimization with Stochastic Feasibility Region with Application to Vehicle Path Planning
Adriano Zanin Zambom, Julian A. A. Collazos, Ronaldo Dias
In real-time trajectory planning for unmanned vehicles, on-board sensors, radars and other instruments are used to collect information on possible obstacles to be avoided and pathw…
A Review of Kernel Density Estimation with Applications to Econometrics
Adriano Zanin Zambom, Ronaldo Dias
Nonparametric density estimation is of great importance when econometricians want to model the probabilistic or stochastic structure of a data set. This comprehensive review summar…
A Hierarchical Model for Aggregated Functional Data
Ronaldo Dias, Nancy L. Garcia, Alexandra M. Schmidt
In many areas of science one aims to estimate latent sub-population mean curves based only on observations of aggregated population curves. By aggregated curves we mean linear comb…