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Gaussian long memory processes

Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes

Wavelet estimation of the long memory parameter for Hermite polynomial of Gaussian processes

... the memory parameter of stochastic processes with long–range ...the memory pa- rameter d goes back to [35] and [15, 16, 17, ...the Gaussian processes, especially the fractional ...

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Application of Malliavin calculus and analysis on Wiener space to long-memory parameter estimation for non-Gaussian processes

Application of Malliavin calculus and analysis on Wiener space to long-memory parameter estimation for non-Gaussian processes

... the processes c H X ct and X t have the same ...stochastic processes are well suited to model physical phenomena that exhibit long ...these processes is the fractional Brownian motion (fBm), ...

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Bayesian nonparametric estimation of the spectral density of a long memory Gaussian time series

Bayesian nonparametric estimation of the spectral density of a long memory Gaussian time series

... with Gaussian weakly dependent data and, in a more general setting, Ghosal and Van der Vaart ...for Gaussian long-memory processes, where the unknown parameters are the spectral density ...

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Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process

Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process

... literature. Gaussian long memory processes lead to complex behaviours, which makes the derivation of con- centration rates a difficult ...short memory part - other than the FEXP ...

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Prediction of long memory processes on same-realisation

Prediction of long memory processes on same-realisation

... Abstract For the class of stationary Gaussian long memory processes, we study some properties of the least- squares predictor of X n+1 based on (X n , . . . , X 1 ). The predictor is obtained ...

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Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes

Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes

... these processes are obtained from the circulant matrix method in case of Gaussian processes or a truncation of an infinite sum in case of non-Gaussian process (see Doukhan et ...following ...

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Adaptive wavelet based estimator of the memory parameter for stationary Gaussian processes

Adaptive wavelet based estimator of the memory parameter for stationary Gaussian processes

... covering long and short memory, in fact larger conditions than those usually required for adaptive log-periodogram or local Whittle estimators) with a nearly optimal convergence ...

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Estimating long memory in volatility

Estimating long memory in volatility

... the memory parameter is the Gaussian semiparametric estima- tor (GSE), introduced by K¨ unsch (1987), and later studied by Robinson (1995b) for processes which are linear in a Martingale difference ...

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Large scale behavior of wavelet coefficients of non-linear subordinated processes with long memory

Large scale behavior of wavelet coefficients of non-linear subordinated processes with long memory

... Inform. Theory 44 (1) (1998) 2–15. [11] P. Abry, D. Veitch, P. Flandrin, Long-range dependence: revisiting aggregation with wavelets, J. Time Ser. Anal. 19 (3) (1998) 253–266. [12] R. Fox, M. S. Taqqu, ...

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Testing fractional order of long memory processes : a Monte Carlo study

Testing fractional order of long memory processes : a Monte Carlo study

... In that latter case, ε t = √ h t ξ t , h t = a 0 +a 1 ε 2 t−1 +b 1 h t−1 , with ξ t a sequence of i.i.d. Gaussian random variables with zero mean and unit variance, a 0 = 1, a 1 = 0.15 and b 1 = 0.8. We consider ...

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Inference for continuous-time long memory randomly sampled processes

Inference for continuous-time long memory randomly sampled processes

... : Long memory, dependence, stationarity, sampled process, time series, limit theorems, Continuous-time Gaussian ...the long-range dependent time series analysis has gained in notoriety and ...

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Testing Fractional Order of Long Memory Processes: A Monte Carlo Study

Testing Fractional Order of Long Memory Processes: A Monte Carlo Study

... In that latter case, ε t = √ h t ξ t , h t = a 0 +a 1 ε 2 t−1 +b 1 h t−1 , with ξ t a sequence of i.i.d. Gaussian random variables with zero mean and unit variance, a 0 = 1, a 1 = 0.15 and b 1 = 0.8. We consider ...

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Aggregation and long memory: recent developments

Aggregation and long memory: recent developments

... aggregated Gaussian process, which may contain a seasonal ...heteroskedastic processes with random coeffcients and common innovations, with a particular emphasis on long memory ...

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Analysis of stationary and non-stationary long memory processes : estimation, applications and forecast

Analysis of stationary and non-stationary long memory processes : estimation, applications and forecast

... stationary processes are composed by four ...stationary processes, focusing on their corresponding self-similar ...of processes: H-self-similar processes (ex: Brownian motion), Gaussian ...

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Bayesian Nonparametric Adaptive Control using Gaussian Processes

Bayesian Nonparametric Adaptive Control using Gaussian Processes

... with Gaussian processes in the context of dual control ...explored Gaussian processes in the context of dual adaptive control [38], ...a Gaussian Process model for learn- ing inverse ...

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Analysis of dance movements using Gaussian processes

Analysis of dance movements using Gaussian processes

... In this paper, we have proposed a method for decomposing dance movements into elementary motions. The approach relies on Gaussian processes allowing for a flexible representation, from extremely coarse to ...

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Interactions between gaussian processes and bayesian estimation

Interactions between gaussian processes and bayesian estimation

... filter Gaussian process (KFGP) ( Reece and Roberts , 2010 ) which naturally reduces the computation by recursively correlating GP priors of different training subsets in an efficient Kalman filter ...

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Zeros of smooth stationary Gaussian processes

Zeros of smooth stationary Gaussian processes

... More precisely, after a proper rescaling, ν R converges almost surely towards the Lebesgue measure in weak-∗ sense. Moreover, the fluctuation of ν R around its mean converges in dis- tribution towards the standard ...

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Nonlinear hyperspectral unmixing using Gaussian processes

Nonlinear hyperspectral unmixing using Gaussian processes

... tion procedure based on GP regression. This method is discussed in the next section. 4. GAUSSIAN PROCESS REGRESSION This section studies a new endmember estimation strategy based on GP regression for nonlinear ...

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On the equivalence of multiparameter Gaussian processes

On the equivalence of multiparameter Gaussian processes

... multiparameter Gaussian processes, that is Gaussian sheets, that are equivalent in law to the Brownian sheet and to the fractional Brownian ...

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