We discuss computational aspects of likelihood-based estimation of univariate ARFIMA(p, d, q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.

This paper analyzes the second order bias of instrumental variables estimators for a dynamic panel model with fixed effects. Three different methods of second order bias correction are considered. Simulation experiments show that these methods perform well if the model does not have a root near unity but break down near the unit circle. To remedy the problem near the unit root a weak instrument...

In recent years many tools and algorithms for model comparison and differencing were proposed. Typically, the main focus of the research laid on being able to compute the difference in the first place. Only very few papers addressed the quality of the delivered differences sufficiently. Hence, this is a general shortcoming in the state-of-the-art. Currently, there are no established community s...

ARFIMA is a time series forecasting model, which is an improve d ARMA model, the ARFIMA model proposed in this article is d emonstrated and deduced in detail. combined with network traffi c of CERNET backbone and the ARFIMA model,the result sho ws that,compare to the ARMA model, the prediction efficiency a nd accuracy has increased significantly, and not susceptible to sa mpling.

This paper presents NeuroChess, a program which learns to play chess from the final outcome of games. NeuroChess learns chess board evaluation functions, represented by artificial neural networks. It integrates inductive neural network learning, temporal differencing, and a variant of explanation-based learning. Performance results illustrate some of the strengths and weaknesses of this approach.

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accur...

Penalized splines are widespread tools for the estimation of trend and cycle, since they allow a data driven estimation of the penalization parameter by the incorporation into a linear mixed model. Based on the equivalence of penalized splines and the Hodrick-Prescott filter, this paper connects the mixed model framework of penalized splines to the WienerKolmogorov filter. In the case that tren...

A stereo vision system for detecting 3D-objects is presented. The method uses a pair of externally uncalibrated cameras and is able t o detect objects located on a staircase. Detection is based on differencing stereo pair images, where a warping transform is used to overlay the left onto the right image, and vice versa. The 3D camera positions are obtained through model t~ased pose estimation a...

This correspondence addresses the asymptotic normal distribution of the sample mean and the sample covariance matrix of mixed spectra time series containing a sum of sinusoids and a moving average (MA) process. Two central limit (CL) theorems are proved. As an application of this result, the asymptotic normal distribution of any sinusoidal frequencies estimator of such time series based on seco...

Oil is the lifeblood of the industrial economy, oil prices are affected by many factors. China is a major industrial country, changes in the price of oil will affect many aspects of economic development, and therefore the price of crude oil research is extremely important. In this paper, monthly average prices of crude oil in Daqing from January 2000 to December 2010 are utilized to do the rese...