continuous-time stationary processes
Spectral Density Estimation of Continuous Time Series
... for time series analysis, it is still one of the most widely used analysis techniques in many branches of sciences, ...strictly stationary time series, the spectral estimation has been studied, ...
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Variance Optimization for Continuous Time Markov Decision Processes
... deterministic stationary policy class, which is different from the mean-variance criterion ...For continuous-time, the variance of the average expected re- turn has been defined in deterministic ...
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Piecewise Deterministic Markov Processes for Continuous Time Monte Carlo
... on continuous-time versions of these Monte Carlo ...example, continuous-time MCMC algorithms have been proposed (Peters and de With, 2012; Bouchard-Cˆ ot´ e et ...a ...
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Randomized and Relaxed Strategies in Continuous-Time Markov Decision Processes
... decision processes (DTMDP) like the linear programming approach developed, ...of stationary randomized ...general, stationary strategies are not sufficient in optimization ...discrete time is a ...
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Numerical Approximation of Fractal Dimension of Gaussian Stochastic Processes
... of stationary Gaussian stochastic processes using the random Euler numerical scheme and based on an ana- lytical formulation of the fractal dimension for filtered stochastic ...of continuous ...
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Modeling long range dependent Gaussian processes with application in continuous time financial models
... the processes having a spectral density of the form ...Gaussian processes, which can be solutions of fractional stochastic differential ...Gaussian processes through estimating the parameters ...
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Piecewise deterministic Markov processes for continuous time Monte Carlo
... its stationary distribu- tion. These continuous-time MCMC algorithms were originally motivated as they are examples of nonre- versible Markov ...these continuous-time MCMC algo- rithms ...
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Bandwidth selection for continuous time Markov processes
... di¤usion processes (see, also, Fan and Zhang, 2003, and Moloche, 2004, for local polynomial estimates under stationarity and recurrence, ...the time span diverges to in…nity, long-span ...a ...
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Persistence probabilities in centered, stationary, Gaussian processes in discrete time
... Generalizing their result, Feldheim and Feldheim [8] showed similar exponential upper and lower bounds for a large class of Gaussian processes in R or Z. Their conditions are similar to ours and their result is ...
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Regularised estimation of 2D locally stationary wavelet processes
... Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an ...assume continuous smoothness ...
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DISCRETE TIME REPRESENTATION OF CONTINUOUS TIME ARMA PROCESSES
... discrete time representations for data generated by a continuous time ARMA( p, q) system when the variables may be stocks, flows, or a combination of the ...discrete time model is ARMA( p, p − ...
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A construction of continuous-time ARMA models by iterations of Ornstein-Uhlenbeck processes
... of processes, the Ornstein-Uhlenbeck processes of order p, denoted ...either stationary continuous-time processes or the series obtained by observing these continuous ...
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Applied Time Series Analysis Part I
... The textbook by Brockwell & Davis (1987, B&D; also 2002) provides some typical examples together with their graphic representation. Most of them are non-economic variables. In detail we see: an alternating ...
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Statistical applications for equivariant matrices
... 2. Applications. Equivariant matrices occur in many scientific phenomena of math- ematics, physics, engineering, etc. We have chosen statistics to be our target of appli- cations. In this section, we present some ...
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Layered continuous time processes in biology
... Irregular time series related by latent processes: evolution of body size in Coccolithus.. Henderiks – Schweder - Reitan.[r] ...
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Stationary Time Series in Pricing
... are the polynomials of the shift operator. In this case, will be called an integrated process of autoregression and a moving average, or ARIMA (p, d, q). This model allows building highly accurate forecasts with a small ...
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Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series
... linear time series model to reflect information about them contained in time series ...of time for forecasting purposes. It is useable in real time applications, because revisions to the ...
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Parameter estimation of continuous-time point processes: Serial dependency and neural applications
... Removal of the negative dependence (long interval follows short and vice versa) was validated by the conditional interval histograms. The three possible causes of the serial dependence c[r] ...
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Importance sampling techniques for estimation of diffusions models
... the continuous-time likelihood ...the processes discussed in Section 4 as proposals has been considered by Golightly and Wilkinson (2008); Chib et ...
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A distance test of normality for a wide class of stationary processes
... P -values for the normality test based on A, computed from 10000 bootstrap replications, are presented in Table 5. For comparison, we also report asymptotic P - values for the test based on B; following Bai and Ng ...
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