Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least-squares estimation of cointegrating regressions between non-stationary and/or long memory stationary variables where the integration orders of regressor and disturbance sum to less than 1, as happens always for stationary regressor...

A new parametric minimum distance time-domain estimator for ARFIMA processes is introduced in this paper. The proposed estimator minimizes the sum of squared correlations of residuals obtained after filtering a series through ARFIMA parameters. The estimator is easy to compute and is consistent and asymptotically normally distributed for fractionally integrated (FI) processes with an integratio...

the artificial accelerograms have been developed for assessing the dynamic response ofstructures. considering seismological properties of the site are necessary for the best simulation ofaccelerograms. the real recorded accelergrams for simulating earthquake phenomenon are used in thearma model. this is due to the fact that the arma model can be considered more advantageous than theothers.in th...

This study evaluates the potential of the entropy rate contour to identify stationary and non-stationary segments of speech signals. The segmentation produced by an entropy rate-based method is compared to the manual phoneme segmentations of the TIMIT and the KIEL corpora. Characteristic points, i.e. steepest rises and falls of the entropy rate curve and its maxima and minima are investigated t...

Abstract-This paper presents the various methods for the spectral analysis of signals for the stationary as well as non-stationary signals. Due to non-stationary characteristics of the signals, it has been always a challenge to achieve time frequency distribution of such signals. Between the various techniques of signal analysis, this paper uses Fourier transform, Short time Fourier transform, ...

Low diversity in a genetic algorithm (GA) can cause the search to become stagnant upon reaching a local optimum. To some extent, non-stationary tasks avoid this problem, which would be a desirable feature of GA for stationary tasks as well. With this in mind, we show that several methods of introducing artificial non-stationary elements help to promote diversity in a GA while working on an inhe...

the stolt (f-k) migration algorithm is a direct (i.e. non-recursive) fourier-domain technique based on a change of variables (or equivalently, a mapping) that converts the input spectrum to the output spectrum. the algorithm is simple and efficient but limited to constant velocity. a v(z)(f-k) migration method, capable of very high accuracy for vertical variations of velocity, can be formulated...

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assumes that the data are generated by a stationary process. However, there are interesting and important circumstances where that assumption will not hold and potential non-stationarity cannot be ignored. Here we introduce a new cl...