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State space modeling of long-memory processes
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
Group LASSO for Structural Break Time Series
Consider a structural break autoregressive (SBAR) process where j = 1, …, m + 1, {t1, …, tm} are change-points, 1 = t0 < t1 < ⋅⋅⋅ < tm + 1 = n + 1, σ( · ) is a measurable function on , and {ϵt} are
On the First-Order Autoregressive Process with Infinite Variance
For a first-order autoregressive process Yt = βYt−1 + ∈t where the ∈t'S are i.i.d. and belong to the domain of attraction of a stable law, the strong consistency of the ordinary least-squares
Spatial Modeling of Regional Variables
Abstract In this article, accumulated sudden infant death syndrome (SIDS) data, from 1974–1978 and 1979–1984 for the counties of North Carolina, are analyzed. After a spatial exploratory data
The Parameter Inference for Nearly Nonstationary Time Series
Abstract A first-order autoregressive (AR) time series Yt = βY t-1 + et is said to be nearly nonstationary if β is close to 1. For a nearly nonstationary AR(1) model, it is shown that the limiting
Inference for Unstable Long-Memory Processes with Applications to Fractional Unit Root Autoregressions
An autoregressive time series is said to be unstable if all of its characteristic roots lie on or outside the unit circle, with at least one on the unit circle. This paper aims at developing
Data mining meets performance evaluation: fast algorithms for modeling bursty traffic
A simple, parsimonious method, the b-model, which solves both problems: it requires just one parameter, and can easily generate large traces, and has many more attractive properties.
This paper develops an empirical likelihood approach for regular generalized autoregressive conditional heteroskedasticity (GARCH) models and GARCH models with unit roots. For regular GARCH models,