6 papers
GARTFIMA Models: A Class of Observation-Driven Models with Tempered Fractional Dynamics
Guilherme Pumi, Sharandeep Singh Pandher, Taiane Schaedler Prass
This paper introduces a class of observation-driven models whose systematic component includes a tempered fractional differencing term. This specification generalizes long-range de…
Estimation of Long-Range Dependent Models with Missing Data: to Impute or not to Impute?
Guilherme Pumi, Gladys Choque Ulloa, Taiane Schaedler Prass
Among the most important models for long-range dependent time series is the class of ARFIMA (Autoregressive Fractionally Integrated Moving Average) models. Estimating the…
A Novel Multiple Imputation Approach For Parameter Estimation in Observation-Driven Time Series Models With Missing Data
Guilherme Pumi, Taiane Schaedler Prass, Douglas Krauthein Verdum
Handling missing data in time series is a complex problem due to the presence of temporal dependence. General-purpose imputation methods, while widely used, often distort key stati…
PRTree: An R Package for Probabilistic Regression Trees with Built-in Missing Data Handling
Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi
PRTree is an R package for fitting Probabilistic Regression Trees (PRTrees), a class of regression trees that replaces deterministic splits with probabilistic associations to produ…
A GARMA Framework for Unit-Bounded Time Series Based on the Unit-Lindley Distribution with Application to Renewable Energy Data
Guilherme Pumi, Danilo Hiroshi Matsuoka, Taiane Schaedler Prass
The Unit-Lindley is a one-parameter family of distributions in obtained from an appropriate transformation of the Lindley distribution. In this work, we introduce a class o…
Order selection in GARMA models for count time series: a Bayesian perspective
Katerine Zuniga Lastra, Guilherme Pumi, Taiane Schaedler Prass
Estimation in GARMA models has traditionally been carried out under the frequentist approach. To date, Bayesian approaches for such estimation have been relatively limited. In the…