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Performance of variance function estimators for autoregressive time series of order one: asymptotic normality and numerical study

Borkowski P., Mielniczuk J.

Control and Cybernetics

2012

Vol. 41, no. 2

415-441

We study performance of several conditional variance estimators for an autoregressive time series which include local linear smoothers with various bandwidths, local likelihood and difference-based estimators. In the theoretical part, asymptotic normality of the local linear estimator of variance with no mixing assumptions imposed on the underlying process is proved. Moreover, numerical examples performed reveal that a two-stage local linear smoother with a bandwidth, proposed by Ruppert, Sheather and Wand, used to estimate the regression function and a simple rule of thumb bandwidth for variance estimation performs best for variances without much structure, whereas the bandwidth considered by Fan and Yao works very well for much more variable variances.

Modele autoregresyjne jako instrument zarządzania zapasami na przykładzie elektrowni cieplnej

Mesjasz-Lech A.

Logistyka

2010

nr 2

W artykule przedstawiono wyniki analizy ekonometrycznej miesięcznych wartości zapasów w trzech kolejnych latach w jednej z elektrowni Południowej Polski. Pod uwagę wzięto te zapasy, których utrzymywanie ma największy wpływ na ogólną działalność finansową elektrowni. Analizie poddano wartość zapasów z wyłączeniem zapasów bezpośrednio produkcyjnych (węgiel, mazut), dla których zbudowano modele autoregresyjne.

In the article effects of the econometric analysis of the monthly stock value in the three next years in one of power stations of Southern Poland were presented. Inventory which has the biggest influence on the general financial activity of the power station was taken into consideration. The value of inventory excluding stock which is direct used in the process of energy production (coal, mazout) was analyzed. The autoregressive moving average models were constructed for distinguished groups of inventory.

Detection of seismic signals on noisy seismograms

Niewiadomski J.

Acta Geophysica Polonica

2002

Vol. 50, nr 2

159-166

In seismologic observational practice seismic signals are very often obscured by a strong seismic noise. Sometimes one needs to have only a very limited knowledge on them, and sole information on the presence of a seismic signal is quite important. Some of such situations include, e.g., seismic source location, where only an onset time is needed, or the situation of monitoring the seismic activity. Below we will concentrate on the really difficult cases, when a visual inspection of a trained seismologist fails to find any hint of the presence of seismic signal on the noisy seismograms. The difficult task of signal detection can be done by a neural network. As the input data we will use the autoregressive parameters which model the seismogram.