activity
20112023
most citedA Review of Kernel Density Estimation with Applications to Econometrics

53 citations · 65 across the 12 of their papers we have counts for

collaborators

12 papers

stat.ME2023

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…

stat.ME2022★ 8 cited

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…

stat.ME2022★ 1 cited

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…

stat.ML2022★ 1 cited

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…

stat.AP2021★ 1 cited

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…

stat.ME2021★ 1 cited

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…