5 citations · 16 across the 4 of their papers we have counts for
4 papers
On the Identifiability of Latent Models for Dependent Data
Stéphane Guerrier, Roberto Molinari
The condition of parameter identifiability is essential for the consistency of all estimators and is often challenging to prove. As a consequence, this condition is often assumed f…
Fast and Robust Parametric Estimation for Time Series and Spatial Models
Stéphane Guerrier, Roberto Molinari
We present a new framework for robust estimation and inference on second-order stationary time series and random fields. This framework is based on the Generalized Method of Wavele…
Wavelet Variance for Random Fields: an M-Estimation Framework
Stéphane Guerrier, Roberto Molinari
We present a general M-estimation framework for inference on the wavelet variance. This framework generalizes the results on the scale-wise properties of the standard estimator and…
The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness
James Balamuta, Roberto Molinari, Stéphane Guerrier +1
The gmwm R package for inference on time series models is mainly based on the quantity called wavelet variance which is derived from a wavelet decomposition of a time series. This…