PPMiss.Package

_PACKAGE

PPMis

The PPMiss package provides functions for parameter estimation of univariate
time series by means of a parametric copula approach. The methodology is based
on the relationship between the process' covariance decay and a parametric
bivariate copulas associated to lagged variables.

internal

Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.

Taiane Schaedler Prass

PPMiss

Copula-Based Estimator for Long-Range Dependent Processes under
Missing Data

Guilherme Pumi

PPMiss.Package function

Taiane Schaedler Prass <a href="/link/taianeprass%40gmail.com?package=PPMiss&version=0.1.1" data-mini-rdoc="PPMiss::taianeprass@gmail.com">taianeprass@gmail.com</a> and Guilherme Pumi <a href="/link/guipumi%40gmail.com?package=PPMiss&version=0.1.1" data-mini-rdoc="PPMiss::guipumi@gmail.com">guipumi@gmail.com</a>

Author

PPMiss: Copula-Based Estimator for Long-Range Dependent Processes under Missing Data. — PPMiss.Package

Taiane Schaedler Prass <a href='mailto:taianeprass@gmail.com'>taianeprass@gmail.com</a> and Guilherme Pumi <a href='mailto:guipumi@gmail.com'>guipumi@gmail.com</a>

PPMiss.Package: PPMiss: Copula-Based Estimator for Long-Range Dependent Processes under Missing Data.

Description

Arguments

Author