activity
20212025
most citedInference on model parameters with many L-moments

4 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

stat.AP2025

A nonstationary seasonal Dynamic Factor Model: an application to temperature time series from the state of Minas Gerais

Davi Oliveira Chaves, Chang Chiann, Pedro Alberto Morettin

In many scientific fields, such as agriculture, temperature time series are of interest both as explanatory variables and as objects of study in their own right. However, at the st…

cs.LG2025

The Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks

Leonardo Fonseca Larrubia, Pedro Alberto Morettin, Chang Chiann

We present the Maximal Overlap Discrete Wavelet Scattering Transform (MODWST), whose construction is inspired by the combination of the Maximal Overlap Discrete Wavelet Transform (…

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.ME2023

Time-varying STARMA models by wavelets

Yangyang Chen, Pedro Alberto Morettin, Chang Chiann

The spatio-temporal autoregressive moving average (STARMA) model is frequently used in several studies of multivariate time series data, where the assumption of stationarity is imp…

stat.ME2022★ 4 cited

Inference on model parameters with many L-moments

Luis Alvarez, Chang Chiann, Pedro Morettin

This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matchin…

stat.ME2021★ 2 cited

Wavelet Estimation for Factor Models with Time-Varying Loadings

Duván Humberto Cataño, C. Vladimir Rodríguez-Caballero, Daniel Peña +1

We introduce a high-dimensional factor model with time-varying loadings. We cover both stationary and nonstationary factors to increase the possibilities of applications. We propos…