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State space modeling of long-memory processes
(INST MATHEMATICAL STATISTICS, 1998)
This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the ...
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, ...
Monte Carlo Test for Stochastic Trend in Space State Models for the Location-Scale Family
(Sociedade Brasileira de Econometria, 2021)
Outliers, structural shifts and heavy-tailed distributions in state space time series models
(Pakistan Journal of Statistics, 2002-12)
In this work a general method is developed for handling outliers, structural shifts and heavy-tailed distributions in linear state space time series models. The basic tool we use for dealing with outliers and structural ...
State-Space Approach to Structural Representation of Perturbed Pitch Period Sequences in Voice Signals
(Mosby-Elsevier, 2015-11)
Objectives: The aim of this study was to propose a state space-based approach to model perturbed pitch period sequences (PPSs), extracted from real sustained vowels, combining the principal features of disturbed real PPSs ...