In this paper we construct a test for the difference parameter d in the fractionally integrated autoregressive moving-average (ARFIMA) model. Obtaining estimates by smoothed spectral regression estimation method, we use the moving blocks bootstrap method to construct the test for d. The results of Monte Carlo studies show that this test is generally valid for certain block sizes, and for these ...

One objective of this paper is to estimate the parameters p,d,q of an autoregressive fractionally integrated moving average ARFIMA(p,d,q) stochastic model by minimizing the squares of the residuals using a Bayesian global optimization techniques. We consider bilinear model, too because it is the simple extension of linear model, defined by adding a bilinear term to traditional ARMA model. There...

We construct geometric shrinkage priors for Kählerian signal filters. Based on the characteristics of Kähler manifold, an algorithm for finding the superharmonic priors is introduced. The algorithm is efficient and robust to obtain the Komaki priors. Several ansätze for the priors are also suggested. In particular, the ansätze related to Kähler potential are geometrically intrinsic priors to th...

This paper illustrates how to compare different agent-based models and how to compare an agent-based model with real data. As examples we investigate ARFIMA models, the probability density function, and the spectral density function. We illustrate the methodology in an analysis of the agent-based model developed by Levy, Levy, Solomon (2000), and confront it with the S&P 500 for a comparison wi...

Researchers in psychology are paying increasing attention to temporal correlations in performance on cognitive tasks. Recently, T. Thornton and D. L. Gilden (2005) introduced a spectral method for analysing psychological time series; in particular, this method is tailored to distinguish transient serial correlations from the persistent correlations characterised by 1/f noise. Thornton and Gilde...

The elevation of Great Salt Lake (GSL) has a great impact on the people of Utah. The flood of GSL in 1982 has caused a loss of millions of dollars. Therefore, it is very important to predict the GSL levels as precisely as possible. This paper points out the reason why conventional methods failed to describe adequately the rise and fall of the GSL levels – the long-range dependence (LRD) propert...