# 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

**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 ...

51

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

**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 ...

36

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

**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

**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 ...

33

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

**the**type

**of**activity or

**the**period

**of**

**the**day, we notice variations

**of**these parameters, see Section ...by

**the**previous example,

**the**

**spectral**...

42

### Time series aggregation, disaggregation and long memory

**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**...

20

### Nonparametric Density Estimation for Multivariate Bounded Data

**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**...

32

### Long term analysis of time series of satellite images

**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 ...

14

### Interactions between gaussian processes and bayesian estimation

**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 ...

168

### Linear prediction of long-range dependent time series

**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 ...

21

### 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 ...

39

### Bayesian nonparametric estimation for Quantum Homodyne Tomography

**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**...

38

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

**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 ...

28

### Linear Prediction of Long-Range Dependent Time Series

**Long**

**memory**, linear model, autoregressive process, forecast error ARMA (autoregressive moving-average) processes are often called short-

**memory**processes be- cause their covariances decay ...

42

### 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 ...

15

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

**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 ...

39

### ESTIMATION OF THE DENSITY OF A DETERMINANTAL PROCESS

**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 ...

25

### Nonparametric estimation of a shot-noise process

**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 ...

5

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

**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- ...

74

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

**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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