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ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors
(Elsevier Ltd, 2016)
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. The adaLASSO is a one-step implementation of the family of folded concave penalized least-squares. ...
Discrete-time autoregressive model for unequally spaced time-series observations.
Most time-series models assume that the data come from observations that are equally spaced in time. However, this assumption does not hold in many diverse scientific fields, such as astronomy, finance, and climatology, ...
Discrete-time autoregressive model for unequally spaced time-series observations.
Most time-series models assume that the data come from observations that are equally spaced in time. However, this assumption does not hold in many diverse scientific fields, such as astronomy, finance, and climatology, ...
Studying the Performance of Cognitive Models in Time Series Forecasting
(Instituto de Informática - Universidade Federal do Rio Grande do Sul, 2020)
Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family
(Sociedade Brasileira de Econometria, 2021)