In this work we are interested in the numerical approximation of 1D parabolic singularly perturbed problems of reaction–diffusion type. To approximate the multiscale solution of this problem we use a numerical scheme combining the classical backward Euler method and central differencing. The scheme is defined on some special meshes which are the tensor product of a uniform mesh in time and a sp...

Cardiovascular magnetic resonance (CMR) perfusion data are suitable for quantitative measurement of myocardial blood flow. The goal of perfusionCMR postprocessing is to recover tissue impulse-response from observed signalintensity curves. While several deconvolution techniques are available for this purpose, all of them use models with varying parameters for the representation of the impulse-re...

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of pa...

A key application of long memory time series models concerns innation. Long memory implies that shocks have a long-lasting eeect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for innation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (...

This paper proposes a novel method for detecting foreground objects in nonstationary complex environments containing moving background objects. We derive a Bayes decision rule for classification of background and foreground changes based on inter-frame color co-occurrence statistics. An approach to store and fast retrieve color co-occurrence statistics is also established. In the proposed metho...

Three ARIMA forecast extension procedures for Census Bureau X-11 concurrent seasona adjustment were empirically tested. Forecasts were obtained from fitted seasonal ARIMA models augmented with regression terms for ouffiers, trading day effects, and Easter effects. Revisions between initia1 and fina seasonaIIy adjusted vaIues were computed. Ranked ANOVAs were used on various revision measures to...

This paper deals with the analysis of long range dependence in the US stock market. We focus first on the log-values of the Dow Jones Industrial Average, Standard and Poors 500 and Nasdaq indices, daily from February, 1971 to February, 2007. The volatility processes are examined based on the squared and the absolute values of the returns series, and the stability of the parameters across time i...

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.