7 citations · 13 across the 9 of their papers we have counts for
11 papers
Consistent Order Selection under Non-Identifiability and Dependence
Eduardo Fonseca Mendes
We provide sufficient conditions for the consistency of penalized least squares procedures that select the order (dimension) of a regression model from a sequence of nested classes…
Estimation risk in conditional expectiles
Marcelo Fernandes, Vitor Henriques, Eduardo Fonseca Mendes
We establish the consistency and asymptotic normality of a two-step estimator of conditional expectiles in the context of conditional scale models. We first estimate the conditiona…
Generalized Information Criteria for Structured Sparse Models
Eduardo F. Mendes, Gabriel J. P. Pinto
Regularized m-estimators are widely used due to their ability of recovering a low-dimensional model in high-dimensional scenarios. Some recent efforts on this subject focused on cr…
Concentration inequalities for high-dimensional linear processes with dependent innovations
Eduardo Fonseca Mendes, Fellipe Lopes
We develop concentration inequalities for the norm of vector linear processes with sub-Weibull, mixingale innovations. This inequality is used to obtain a concentration…
Machine Learning Advances for Time Series Forecasting
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alter…
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted…
Markov Interacting Importance Samplers
Eduardo F. Mendes, Marcel Scharth, Robert Kohn
We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) appro…
An extended space approach for particle Markov chain Monte Carlo methods
Christopher K. Carter, Eduardo F. Mendes, Robert Kohn
In this paper we consider fully Bayesian inference in general state space models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an augmented model that takes int…
A flexible Particle Markov chain Monte Carlo method
Eduardo F. Mendes, Christopher K. Carter, David Gunawan +1
Particle Markov Chain Monte Carlo methods are used to carry out inference in non-linear and non-Gaussian state space models, where the posterior density of the states is approximat…
Convergence Rates for Mixture-of-Experts
Eduardo F. Mendes, Wenxin Jiang
In mixtures-of-experts (ME) model, where a number of submodels (experts) are combined, there have been two longstanding problems: (i) how many experts should be chosen, given the s…
Model Selection Consistency for Cointegrating Regressions
Eduardo F. Mendes
We study the asymptotic properties of the adaptive Lasso in cointegration regressions in the case where all covariates are weakly exogenous. We assume the number of candidate I(1)…