A new algorithm for optimal interpolation of discrete-time stationary processes

2006 ◽  
pp. 699-718 ◽  
Author(s):  
Michele Pavon
Keyword(s):  
Discrete Time ◽  
Optimal Interpolation ◽  
Stationary Processes

Simulation of Real Discrete Time Gaussian Multivariate Stationary Processes with Given Spectral Densities

2015 ◽  
Vol 36 (6) ◽  
pp. 783-796 ◽  
Author(s):  
M. Azimmohseni ◽  
A. R. Soltani ◽  
M. Khalafi
Keyword(s):  
Discrete Time ◽  
Stationary Processes ◽  
Spectral Densities

Generalized Levinson–Durbin Sequences and Binomial Coefficients

Integers ◽  
2009 ◽  
Vol 9 (2) ◽  
Author(s):  
Paul Shaman
Keyword(s):  
Stochastic Process ◽  
Sample Size ◽  
Mean Square Error ◽  
Discrete Time ◽  
Minimum Mean Square Error ◽  
Stationary Stochastic Process ◽  
Binomial Coefficients ◽  
Stationary Processes ◽  
Mean Square ◽  
Special Case

AbstractThe Levinson–Durbin recursion is used to construct the coefficients which define the minimum mean square error predictor of a new observation for a discrete time, second-order stationary stochastic process. As the sample size varies, the coefficients determine what is called a Levinson–Durbin sequence. A generalized Levinson–Durbin sequence is also defined, and we note that binomial coefficients constitute a special case of such a sequence. Generalized Levinson–Durbin sequences obey formulas which generalize relations satisfied by binomial coefficients. Some of these results are extended to vector stationary processes.

Performance of Discrete-Time Predictors of Continuous-Time Stationary Processes.

1985 ◽  
Author(s):  
Stamatis Cambanis ◽  
Elias Masry
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Stationary Processes

A power-law model and other models for long-range dependence

1997 ◽  
Vol 34 (3) ◽  
pp. 657-670 ◽  
Author(s):  
R. J. Martin ◽  
A. M. Walker
Keyword(s):  
Long Range ◽  
Discrete Time ◽  
Power Law ◽  
Long Range Dependence ◽  
Stationary Processes ◽  
Power Law Model ◽  
Slow Decay ◽  
Exact Power ◽  
Correlation Structures ◽  
Discrete Time Models

It is becoming increasingly recognized that some long series of data can be adequately and parsimoniously modelled by stationary processes with long-range dependence. Some new discrete-time models for long-range dependence or slow decay, defined by their correlation structures, are discussed. The exact power-law correlation structure is examined in detail.

scholarly journals Persistence of non-Markovian Gaussian stationary processes in discrete time

2018 ◽  
Vol 97 (4) ◽  
Author(s):  
Markus Nyberg ◽  
Ludvig Lizana
Keyword(s):  
Discrete Time ◽  
Stationary Processes

A power-law model and other models for long-range dependence

1997 ◽  
Vol 34 (03) ◽  
pp. 657-670 ◽  
Author(s):  
R. J. Martin ◽  
A. M. Walker
Keyword(s):  
Long Range ◽  
Discrete Time ◽  
Power Law ◽  
Long Range Dependence ◽  
Stationary Processes ◽  
Power Law Model ◽  
Slow Decay ◽  
Exact Power ◽  
Correlation Structures ◽  
Discrete Time Models

It is becoming increasingly recognized that some long series of data can be adequately and parsimoniously modelled by stationary processes with long-range dependence. Some new discrete-time models for long-range dependence or slow decay, defined by their correlation structures, are discussed. The exact power-law correlation structure is examined in detail.

scholarly journals Model fitting for continuous-time stationary processes from discrete-time data

1992 ◽  
Vol 41 (1) ◽  
pp. 56-79 ◽  
Author(s):  
Keh-Shin Lii ◽  
Elias Masry
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Model Fitting ◽  
Stationary Processes ◽  
Time Data
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