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Haut PDF 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

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

... define a long memory process as one such that its spectral density f (λ) can be written as the product of a slowly varying function ˜ f (λ) and the quantity ...

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On the spectral density of the wavelet coefficients of long memory time series with application to the log-regression estimation of the memory parameter

On the spectral density of the wavelet coefficients of long memory time series with application to the log-regression estimation of the memory parameter

... estimate the memory parameter using wavelet analysis have gained popularity in many areas of ...use, a rigorous semi-parametric asymptotic theory, comparable to the one developed for ...

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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

... 5.2. The conditions given in Theorem ...rates of convergence of the posterior distribution in the ...case. The first condition is a condition on the prior mass ...

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Locally stationary long memory estimation

Locally stationary long memory estimation

... In the aforementioned spirit of semi-parametric modelling, and in contrast to the parametric approach of [3], one of the very few existing approaches on time-varying ...

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A nonparametric estimator of the spectral density of a continuous-time Gaussian process observed at random times

A nonparametric estimator of the spectral density of a continuous-time Gaussian process observed at random times

... to the type of activity or the period of the day, we notice variations of these parameters, see Section ...by the previous example, the spectral ...

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Time series aggregation, disaggregation and long memory

Time series aggregation, disaggregation and long memory

... find the individual processes (if they exist) of form ...mixture density ϕ, which produce the aggregated process, then we call this problem a disaggregation ...problem. The ...

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Nonparametric Density Estimation for Multivariate Bounded Data

Nonparametric Density Estimation for Multivariate Bounded Data

... summarize the main findings for each model separately. For model A, the Gaussian kernel estimator is the best since there are no observations in the boundary ...terms of ...

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Long term analysis of time series of satellite images

Long term analysis of time series of satellite images

... Since the two last decades, satellites acquired a global coverage of the earth with a short revisit ...time. The two satellites of the MODIS 1 program are ...

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Interactions between gaussian processes and bayesian estimation

Interactions between gaussian processes and bayesian estimation

... recursive Bayesian in- ference is coupled with an active set selection mechanism to balance the tradeoff between accuracy and ...efficiency. The most attractive point of SOGP is its online ...

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Linear prediction of long-range dependent time series

Linear prediction of long-range dependent time series

... study the behaviour of the mean-squared errors as k tends to infinity as [ 15 ] does for short memory ...15 The paper is organised as ...study the best linear predictor knowing ...

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Bayesian nonparametric estimation for Quantum Homodyne Tomography

Bayesian nonparametric estimation for Quantum Homodyne Tomography

... of a normal distribution on R 2 with covariance matrix diag(1/2, 1/2) and the uniform distribution on [0, ...iterations of the algorithm for n = 500, n = 2000 and n = 5000 simulated ...

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Bayesian nonparametric estimation for Quantum Homodyne Tomography

Bayesian nonparametric estimation for Quantum Homodyne Tomography

... state the embedding C g (β, r, L) ⊆ A(β/2, r, ...in the intersection of a class A(β/2, r, L) with the set of pure states, and it makes sense to compare the ...

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Bayesian nonparametric learning of switching dynamics in cohort physiological time series: Application in critical care patient monitoring

Bayesian nonparametric learning of switching dynamics in cohort physiological time series: Application in critical care patient monitoring

... in the same manner [ 16 ]. As the focus of this current investigation is on the prognostic value of the common (instead of rare) dynamic behaviors, the proposed ...

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Linear Prediction of Long-Range Dependent Time Series

Linear Prediction of Long-Range Dependent Time Series

... Keywords: Long memory, linear model, autoregressive process, forecast error ARMA (autoregressive moving-average) processes are often called short-memory processes be- cause their covariances decay ...

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Bayesian conditional Monte Carlo Algorithm for nonlinear time-series state estimation

Bayesian conditional Monte Carlo Algorithm for nonlinear time-series state estimation

... Bayesian Conditional Monte Carlo Algorithms for non linear time-series state estimation Yohan Petetin*, Franc¸ois Desbouvries, Senior Member, IEEE Abstract—Bayesian filtering aims at ...

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Rates of convergence for the posterior distributions of mixtures of Betas and adaptive nonparametric estimation of the density

Rates of convergence for the posterior distributions of mixtures of Betas and adaptive nonparametric estimation of the density

... (1.5) The difficulty with mixture models comes from the fact that it is of- ten quite hard to obtain precise approximating properties for these ...descriptions of the Kullback-Leibler ...

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ESTIMATION OF THE DENSITY OF A DETERMINANTAL PROCESS

ESTIMATION OF THE DENSITY OF A DETERMINANTAL PROCESS

... use a test possessing robustness properties in view of selecting the closest element to Π among the Π m ...detail the statistical procedure here and rather refer the reader to ...

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Nonparametric estimation of a shot-noise process

Nonparametric estimation of a shot-noise process

... to the empiri- cal characteristic function associated to the shot-noise obser- vations X 1 , .... The general framework of those inverse problems is developed in [8] where the authors ...

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Large random matrix approach for testing independence of a large number of Gaussian time series

Large random matrix approach for testing independence of a large number of Gaussian time series

... On the literature The problem of testing whether various jointly stationary and jointly Gaussian time series are uncorrelated is an important problem that was extensively ad- ...

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Detecting changes in the fluctuations of a Gaussian process and an application to heartbeat time series

Detecting changes in the fluctuations of a Gaussian process and an application to heartbeat time series

... signals of Athlete 1 in ms, Hertz and BPM (up), of Athletes 2, 3 and 4 in BPM (down) Numerous authors have studied heartbeat time series (see for instance [24], [25] or ...[3]). A model ...

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