Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

This paper reviews alternative methods for estimation for stationary Gegenbauer processes
specifically, as distinct from the more general long memory models. A short set of Monte …

Classical assessments of temperature trends are based on the analysis of a small number of
time series. Considering trend to be only smooth changes of the mean value of a stochastic …

Gegenbauer processes and their generalizations represent a general way of modeling long
memory and seasonal long memory; they include ARFIMA, seasonal ARFIMA, and GARMA …

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and
wind speeds, have shown evidence of long memory (LM). LM implies that these quantities …

Suppose that we have a historical time series with samples taken at a slow rate, eg
quarterly. The paper proposes a new method to answer the question: is it worth sampling the …

The property of long memory and its parameter estimate are important in financial
econometrics. This paper proposes a novel Bayesian approach to estimate the fractional …

Gaussian time-series models are often specified through their spectral density. Such models
present several computational challenges, in particular because of the nonsparse nature of …

In this work, we will investigate a Bayesian approach to estimating the parameters of long
memory models. Long memory, characterized by the phenomenon of hyperbolic …

Vector autoregressive models have become a staple in the analysis of multivariate time
series and are formulated in the time domain as difference equations, with an implied …